Filter structure for iterative signal processing

ABSTRACT

A method is for communicating by tracking time varying channels in a multiple access packet based communication network. Each packet includes a preamble portion including a data symbol, and a data portion including data symbols. The method may include initializing a channel estimate reference from an initial channel estimate based upon the data symbol in a preamble portion of a received packet, and updating the channel estimate reference based upon a channel estimate of a current data symbol and a previously received data symbol from the data symbols in a data portion of the received packet. The method may also include repeating the updating upon receipt of a subsequent data symbol from the data symbols in the data portion of the received packet.

RELATED APPLICATIONS

This application is a division of U.S. patent application Ser. No.10/897,886 filed 23 Jul. 2004, which claims benefit of and is acontinuation-in-part of co-pending international application No.PCT/AU03/00502 entitled “Filter Structure for Iterative SignalProcessing”, filed 29 Apr. 2003, now WO 03/094037, which takes priorityfrom Australian Provisional Patent Application No. PS2053, filed 2 May2002, also entitled “Filter Structure for Iterative Signal Processing”and further claims benefit from Australian Provisional PatentApplication No. 2003903826, filed 24 Jul. 2003, entitled “An OFDMReceiver Structure”. The specifications of and, Internationalapplication (PCT) are Incorporated herein by reference in their entiretyand for all purposes.

FIELD OF INVENTION

The present invention relates to the field of wireless communications.In particular, the present invention relates to improved multiple accesscommunications. In one form, the invention relates to an improved signalprocessing method and apparatus for a multiple access communicationsystem. It will be convenient to hereinafter describe the invention inrelation to the use of an iterative method of determining the receptionof a signal in a multi user packet based wireless OFDM (OrthogonalFrequency Division Multiplexing) communication system, however, itshould be appreciated that the present invention may not be limited tothat use, only. By way of further example, in other forms the presentinvention may relate to recursive filtering for joint iterative decodingin a variety of systems and functions such as linear multiple accesschannel decoders, iterative equalisation, iterative joint channelestimation and detection/decoding, iterative space-time processing,iterative multi user interference cancellation and iterativedemodulation.

RELATED ART

Throughout this specification the use of the word “inventor” in singularform may be taken as reference to one (singular) or more (plural)inventors of the present invention. The inventor has identified thefollowing related art.

Most wireless communications systems are based on so-called multipleaccess techniques in which, information such as voice and data arecommunicated. This is a technology where many simultaneously activeusers share the same system resources in an organised manner. In mostcases, sharing resources in a multiple access system means that if morethan one user is active, then all active users interfere with eachother. Traditionally, such interference has been considered to be partof the inevitable noise that corrupts transmissions.

Such interference increases with the number of active users and thus,the performance quality in terms of how many users (capacity) that canshare the resources simultaneously becomes limited.

FIG. 1 shows an exemplary multiple access scenario that may occur inWireless Networks. The radio terminals 102, 104 and 100 b transmitsignals that are received at network access point 100 a. In general notall of these signals are intended for radio terminal 100 a. They maybesignals from devices that belong to other networks, presumably inunlicensed radio spectrum. In any case there are ordinarily some usersof interest that belong to the network to which 100a provides access.The Network aims to make arrangements for all of these signals to beeffectively transmitted. Commonly the users may be required to share theradio resource by, for example, transmitting on different frequencies orat different times. Such techniques may be wasteful in terms of theexpensive radio resource.

The radio terminal 102 may have an associated user 103 who generates andreceives information (in the form of voice, video, data etc). Similarly,the radio terminal 102 is associated with a user. In the case of avehicular user 105, the vehicle (such as bus, train, or car) maygenerate and receive data to be communicated over the network. This datamay also be generated and received by the passengers and/or operators ofthe vehicle. The network access point 100 b may also wish to communicatewith radio terminal 100 a as may be the case in wireless backhaul ormultihop networks. In this respect, it is also possible that the otherusers' radio terminals 102, 104 may form part of any multihoppingnetwork.

One way to improve capacity is to introduce error control coding.Applying coding allows performance to be improved by only allowing a fewof all possible combinations of code symbols to be transmitted. Anotherway is to exploit the information contained in the interference. This isknown as joint multiuser detection. In systems where both thesetechniques are used, a decoding strategy may be applied which is termediterative decoding. Here, a multiuser detector first provides anestimate of the transmitted symbols in terms of reliability information.This information is forwarded to decoders that also provide reliabilityinformation based on the input from the detector. Information is thenexchanged in an iterative fashion until there are no furtherimprovements. This decoding strategy may increase capacitysignificantly, getting very close to theoretical capacity limits at acomplexity level within reach of practical implementation. However, anoptimal multiuser detector is prohibitively complex for practicalimplementation, as the inherent complexity grows exponentially with thenumber of active users. Instead, linear multiuser detection based onlinear filtering may be applied, where the corresponding complexity onlygrows linearly with the number of active users. The inventor hasidentified that for practical reasons related art linear filters foriterative joint multiuser decoding are based on the received signal andthe most recent information from the decoders as input to the filter.These filters have been designed based on various optimality criteria.

Where multiple users share common communications resources, access tochannel resources may be addressed by a multiple access scheme, commonlyexecuted by a medium access control (MAC) protocol. Channel resourcessuch as available bandwidth are typically strictly limited in a wirelessenvironment. It is therefore desirable to use these resources asefficiently as possible. Allowing multiple users to share commonresources creates a risk for disturbances and interference caused bycolliding access attempts. Such disturbances are usually referred to asmultiple access interference. In wireless local area network (WLAN)systems the MAC attempts to schedule transmissions from Stations inorder to avoid collisions. Sometimes the MAC fails, and Stations accessthe channel resources simultaneously. An example of this situation isillustrated in FIG. 2, which shows the transmission of packets from afirst transmitter station 1 a second transmitter station 2 and, arepresentation of received packets at the access point shown on thelowermost line. Physical layer receivers may fail to recover suchcollided packets. As the traffic load on the network increases, thisproblem becomes a significant limiting factor in terms of networkcapacity and quality of service.

A different problem, leading to similar effects, is caused by themultipath nature of communication channels associated with, for example,a WLAN. The multipath channel causes several delayed replicas of thesame signal to arrive at the receiver. This, in turn, createsself-interference similar in nature to multiple access interferencediscussed above. In this case, the problem becomes a limiting factor forthe required power to achieve acceptable performance, which translatesinto limitations on the coverage of the WLAN. An example of a direct anda reflected version of the original signal arriving at the receiver isshown in FIG. 3, where the direct and reflected transmissions of thepacket are illustrated on the top two lines as shown. The presence ofself interference is indicated by shading in the received signal,represented by the access point on the lowermost line as shown.Transmission range may be affected by the interference mechanismsdescribed above and also by the sophistication of the diversity signalprocessing at the Receiver. Physical Layer receiver designers thereforestrive to ensure that effective use is made of all available time,frequency and space diversity (the latter may be provided through theuse of multiple antennas).

The inventor has also identified that when synchronizing transmittedpackets over wireless connections each packet ordinarily has a preambleof several repetitions of the same short signal. A received packetsignal may be correlated with a delayed version of itself where commonlythe delay equals the duration of the repeated signal component in thepreamble. This correlation may be implemented repetitively over a givensample sequence. The output power of the resultant correlation may thenbe combined with the average power of the raw received signal to definea decision statistic. The point at which the decision statistic exceedsa given threshold is selected as the time of arrival of the packet.However, there are drawbacks with this technique in as much as signaldistortions may be amplified or accentuated by the processing involvedwith the synchronization process producing uncertainties in thedetermination of packet timing.

Generally, in packet based communication systems it is important toreduce latency of a receiver or, in other words, provide as little delayas possible between arrival of signals and the decoding of the bitscontained in those signals. Moreover, receiver processes are unable todetermine the variation of a radio channel over the time of a packetlength and the associated effect on the waveform of the transmittedsignal. This may lead to lower than optimum data rates due to poorlytracked packets that are otherwise intact being discarded.

In OFDM packet based communication systems channel impairments mayoccur, which contribute to changing both the channel over which an OFDMsignal travels and also the received signal itself. Collectively, thesechannel impairments comprise variations in the transmission channel dueto multipath fading and, variations to OFDM symbols due to frequency andtime offsets caused by receiver inaccuracies and phase offsets due tocombined transmission and reception processes. These channel impairmentsmay vary from OFDM symbol to OFDM symbol, in other words, they may notbe invariant over the length of a packet. Traditionally, channelimpairments are countered by estimates made using a packet preamble andmaintained by pilot symbols throughout the received packet, which mayassume invariance over the packet length. Other methods use dataestimates to aid for example with channel estimation and these areimplemented in the frequency domain and may result in power loss bydiscarding a cyclic prefix for each received symbol. Generally, there isno use made of all available received information to address channelimpairments in such packet based communication systems.

With regard to space diversity, for multiple receiving antennae inwireless data packet communication systems related art schemes providedecisions on the synchronization of a received signal on the basis ofper antenna and then a majority vote, otherwise the receivedmeasurements are added prior to the decision. These approaches do notaddress the variation of signal statistics across the number of antennaeresulting in degraded synchronization accuracy and increased packetloss.

In EP 1387544 it is noted that time synchronisation of a receiver to theincoming signal is essential for effective decoding of that signal. Inmany packet based applications a special preamble is inserted by thetransmitter at the start of every packet transmitted in order to assistthe receiver with its timing estimation task. In OFDM systems thetransmitter imparts a special structure on the signal called a cyclicprefix. This cyclic prefix is inserted for every OFDM symbol. A cyclicprefix is a replica of a small portion of the last section of a signalinserted at the start of the signal. There are many OFDM symbolstransmitted sequentially in most forms of communication. In EP 1387544the cyclic prefix, in the form of a guard interval as a cycliccontinuation of the last part of the active symbol, is employed to timesynchronise the receiver instead of a preamble. In EP 1387544 a two steptime synchronisation approach is disclosed, namely a pre-FFT andpost-FFT time synchronisation algorithm. These are complementarytechniques and may be used together. The pre-FFT technique consists of a“delay and correlate” algorithm applied to find the cyclic prefix of theOFDM symbols. This is achieved by setting the delay in the “delay andcorrelate” algorithm to the distance between the cyclic prefix and theregion from which it was copied. The output of the correlator is thenfiltered using an auto-regression filter comprising a recursiveInfinite-Impulse Response (IIR) filter to determine an average of thecorrelation across OFDM symbols. A second filtering, by way of smoother44 in FIG. 2 of EP 1387544, is then applied to discard samples outsideof the maximum delay measurable, namely, the cyclic prefix duration.However, EP 1387544 relates to a system which makes use of a streamingsignal and not readily adapted for the random arrival of packets. In thecase of streaming signal, the signal is always there but the fine timingassociated with the OFDM symbol boundaries must be determined.

In U.S. Pat. No. 6,327,314 (Cimini, Jr. et al) the problem of trackingthe radio channel in a hostile propagation environment is addressed forwireless communications systems using OFDM and one or more antennae forreception. The solution disclosed by Cimini Jr. employs decoder anddemodulator outcomes to generate a training or, reference signal, todrive the estimation of the channel for use in decoding the next symbol.The decoding, demodulation and channel estimation loops run according tothe paradigm that the channel estimate may use all outcomes up to andincluding the symbol to be decoded. Each OFDM symbol is decoded once.The raw channel estimate is obtained by multiplying the received OFDMsymbol with the training symbols. These training symbols may be from adecoding step. The raw channel estimate, corresponding to one OFDMsymbol, is stored in a database. Each time a new OFDM symbol is to beprocessed all raw estimates in the database are employed to yield thechannel estimate at the processing wavefront. In this disclosure the rawchannel estimates are stored and a smoothing step is executed every timethe data base is accessed, which entails a relative degree ofcomplexity.

In U.S. Pat. No. 6,477,210 (Chuang et al) the problem of tracking theradio channel in a hostile propagation environment is also addressed forwireless communications systems using OFDM and one or more antennae forreception. The solution provided in this disclosure augments thatdisclosed in U.S. Pat. No. 6,327,314 by more clearly disclosing theprocessing flow and adding a backward recursion to the processing. Thebackward recursion includes the steps of demodulation, decoding andchannel estimation, as in the forward recursion, but the processingcommences from the end of the packet. Chuang et al is restricted toMaximum Likelihood decoding systems such as Viterbi decoders. There aremany other types of FEC systems that do not employ ML decoding (e.g.Soft Output Decoders such as A-Posterior Probability techniques) and,moreover, for which Chuang is not adapted to operate within.

In a paper by Czylwik, A., entitled “Synchronization for systems withantenna diversity”, IEEE Vehicular Technology Conference, Vol. 2, 19-22Sep. 1999, pp 728-732 the time and frequency synchronisation of areceiver is considered. In order to successfully decode a packet thereceiver must determine the packet time of arrival. Errors in thisestimate may result in signal power loss or failures in thesynchronisation of high layer structures such as error control codingand FFT windows. Another parameter to be estimated is residual frequencyoffset. This parameter must be accurately estimated and its effectremoved or countered if the packet is to be decoded. Errors in thisestimate may result in demodulator failure and subsequent packet decodefailure. When a receiver has two antennae there is a possibility toemploy these two signals to improve estimation of time and frequencyoffsets. As disclosed in Czylwik, conventional techniques for singleantenna exist involving the calculation and subsequent combination oftwo components. In this paper two main methods are proposed for time andfrequency offset estimation. In the first, one antenna is selected,based on received power strength, and conventional techniques areapplied to only that signal. In the second method disclosed by Czylwik,first and second conventional components are computed for each antenna.The two first components from each antenna are added. The two secondcomponents from each antenna are added. The resulting sums are thentreated conventionally as a first and second component. The option ofweighting each component prior to combining across antenna according toa signal strength measure for each corresponding antenna is alsodisclosed in Czylwik. This later option is shown to perform better thanany of the other proposals in the paper. Filtering of the resultingmetric for time synchronisation is also disclosed.

Any discussion of documents, devices, acts or knowledge in thisspecification is included to explain the context of the invention. Itshould not be taken as an admission that any of the material forms apart of the prior art base or the common general knowledge in therelevant art in Australia, the United States of America or elsewhere onor before the priority date of the disclosure and claims herein.

SUMMARY OF INVENTION

It is an object of the present invention to overcome or mitigate atleast one of the disadvantages of related art systems.

In one form the present invention provides an iterative decoding circuitfor a wireless multiuser communications receiver comprising:

a first signal processing means for receiving at least one receivedsignal, said first signal processing means comprising at least twolinear iterative filters such that:

the first linear iterative filter provides an estimate of a selectedreceived signal to an estimated signal output and;

a second linear iterative filter provides estimates of at least oneother received signal, delayed by one iteration cycle, to an input ofsaid first linear iterative filter;

a second signal processing means for receiving the estimated signaloutput of the first linear iterative filter and providing a furtherreceived signal estimate to the input of the first signal processingmeans in a succeeding iteration cycle of the decoding circuit.

In another form the present invention provides a method, apparatus andsystem of communicating in a multiple access network by iterativelyreceiving multi user signals comprising:

determining a first set of signal estimates for the multi user signalsbased on linear channel constraints;

determining a second set of signal estimates based on non-linear channelconstraints and the first set of signal estimates;

providing the second set of signal estimates as input to the step ofdetermining the first set of signal estimates;

repeating the above steps at least once.

In a further form the present invention provides an iterative receiverfor receiving multi user signals comprising:

a first signal processing component for determining a first set ofsignal estimates for the multi user signals based on linear channelconstraints;

a second signal processing component for receiving the first set ofsignal estimates and determining a second set of signal estimates basedon non-linear channel constraints;

wherein the signal processing components are operatively connected so asto provide the second set of signal estimates as input to the firstsignal processing component in a succeeding iteration cycle.

In yet another form the present invention provides an iterative signalprocessing arrangement having:

one or more pairs of first and second signal processing components, thepairs of components being in iterative configuration, each of the firstsignal processing components having as input one or more receivedsignals dependent upon one or more transmitted signals, wherein for eachsaid signal processing component pair the output of said first signalprocessing component is an estimate of a characteristic of a selectedtransmitted signal based on the current and one or more previous inputsignals received by said first signal processing component, which isinput to said corresponding second signal processing component thatprovides a further estimate of said selected transmitted signal to theoutput of said second signal processing component, the outputs of allsaid second signal processing components of respective pairs are inputto each said first signal processing components of all said pairs in asucceeding iteration cycle

In still another form the present invention provides a method, apparatusand system of communicating in a multiple access network by iterativelyreceiving OFDM packets comprising:

a) sample a receiver input signal;

b) add the input signal with one of a plurality of prior stored receivedpacket sample estimates to determine a packet sample hypothesis;

c) determine an information bit estimate from the sample hypothesis forstorage in an information bit estimates list;

d) determine an updated received packet sample estimate from the samplehypothesis for updating the plurality of prior stored estimates;

e) subtract the updated sample estimate from the sample hypothesis todetermine a noise hypothesis and provide the noise hypothesis as thereceiver input signal;

f) repeat steps a) to e) until at least one or more complete packets areaccumulated in the information bit estimates list.

In yet another form the present invention provides a method, apparatusand system of communicating in a multiple access network by iterativelyproviding a sample estimates list in an OFDM receiver comprising:

a) sample a receiver input signal;

b) determine a packet sample estimate from the sampled receiver inputsignal;

c) store the packet sample estimate;

d) determine a packet sample hypothesis by adding the receiver inputwith a selected previously stored packet sample estimate;

e) determine an updated packet sample estimate by decoding andre-transmission modelling the packet sample hypothesis;

f) update the selected previously stored packet sample estimate with theupdated packet sample estimate.

In still another form the present invention provides a method, apparatusand system of communicating in a multiple access network by iterativelyproviding a packet information bit estimates list in an OFDM receivercomprising:

a) determine a packet sample hypothesis by adding a receiver input witha selected previously stored packet sample estimate;

b) determine an information bit estimate by decoding the packet samplehypothesis with one or more of a hard decoding technique and a softdecoding technique

c) storing the information bit estimate with one or more previouslydetermined information bit estimates;

d) repeating steps a) to c) until a complete packet is accumulated.

In yet another form the present invention provides a method, apparatusand system of communicating in a multiple access network includingdetermining a hybrid OFDM received packet sample estimate comprising:

multiplexing a time domain channel application received sample estimatewith a frequency domain channel application received sample estimate,such that the multiplexed time domain sample estimate is mapped tocorrespond to one or more of:

an OFDM signal cyclic prefix;

an OFDM tail portion, and;

an OFDM guard period,

and wherein the multiplexed frequency domain sample estimate is mappedto correspond to one or more of:

an OFDM signal preamble and;

an OFDM payload data symbol.

In another form the present invention provides a method, apparatus andsystem of communicating in an OFDM multiple access network comprising:

performing multi-user interference cancelling which comprises adapting asingle pass OFDM receiver to iteratively receive signals at the samplinglevel so as to allow the receiver to differentiate a desired packet froman observation of an interference signal at the receiver input.

In yet another form the present invention provides a method, apparatusand system of communicating in a multiple access communication networkby synchronizing packets arriving at a receiver comprising:

receiving a packet input signal;

determining a correlation signal corresponding to the packet inputsignal;

processing the input and correlation signals such that at least one ofthe input signal and the correlation signal are filtered;

determining a decision statistic by combining a power component of theprocessed correlation signal with a power component of the processedinput signal;

nominate a point in time given by a predetermined threshold condition ofthe decision statistic as a received packet arrival time.

In yet another form the present invention provides a method, apparatusand system of communicating by tracking time varying channels in amultiple access packet based communication network comprising:

a) initializing a channel estimate reference based on an initial channelestimate in a received packet preamble;

b) updating the channel estimate reference based on a packet data symbolchannel estimate in a coded portion of the current and all priorreceived data symbols;

c) repeating step b) at the arrival of subsequent packet data symbols.

In yet another form the present invention provides a method, apparatusand system of communicating by estimating time varying channelimpairments in a multiple access packet based communication network,where channel impairments comprise channel variation, signal frequencyoffset and signal time offset, comprising:

a) initializing a set of channel impairment estimates based on initialpilot and preamble symbols included in a received packet;

b) performing a decoder operation which comprises processing the set ofchannel impairment estimates and the received packet to determine a setof transmit symbol estimates;

c) updating the set of channel impairment estimates with the determinedset of symbol estimates and received packet;

d) repeating steps b) and c).

In still another form the present invention provides a method, apparatusand system of communicating in a multiple access network by time varyingchannel estimation in a receiver for receiving transmitted packets,comprising:

a) estimating a frequency offset based on information included in areceived packet preamble;

b) correcting a received signal using the estimated frequency offset;

c) determining a channel estimate using information included in thereceived packet preamble;

d) transforming a sample sequence of the received signal into thefrequency domain such that the sample sequence includes OFDM symbols andintervening cyclic prefixes;

e) performing a decoding operation which comprises processing thedetermined channel estimate and received packet;

f) generating a transmission sample sequence using the decoding resultsand information in the received packet preamble;

g) transforming the transmission sample sequence into the frequencydomain;

h) updating the determined channel estimate by combining the receivedsample sequence and the transmission sample sequence in the frequencydomain;

i) repeating steps e) to h).

In a preferred embodiment, the combining operation of step h), whichupdates the determined channel estimate, is performed by dividing thereceived sample sequence and the transmission sample sequence in thefrequency domain.

In a further form the present invention provides a method, apparatus andsystem of communicating in a multiple access network by time varyingchannel estimation in a receiver for receiving transmitted packets,where the receiver retrieves OFDM symbols from a received signal andtransforms the retrieved symbols to the frequency domain, comprising:

a) determine a matrix of training symbols comprised of symbol estimatesderived from a decoder;

b) determine a matrix of frequency domain received OFDM symbols;

c) determine an intermediate channel estimate matrix by multiplying theOFDM symbol matrix by the conjugate of the training symbol matrix;

d) determine an intermediate matrix of training weights comprising theabsolute value of the training symbol matrix;

e) perform a smoothing operation on both intermediate matricescomprising 2 dimensional filtering;

f) determine the channel estimate by dividing the smoothed channelestimate matrix with the smoothed training weight matrix.

In embodiments of the invention, the step d) determining an intermediatematrix of training weights may comprise other functions such as, forexample, (absolute value of the training symbol matrix)².

In still another form the present invention provides a method, apparatusand system of communicating in a multiple access network by estimatingoffsets in a receiver for receiving transmitted packets, comprising:

a) determine a matrix of received OFDM symbols;

b) determine a matrix of conjugated data symbols wherein the datasymbols comprise one or more of preamble, training and estimatedsymbols;

c) determine a 2 dimensional Fourier transform matrix comprised of thereceived symbol matrix multiplied with the conjugated symbol matrix;

d) filter the Fourier transform matrix;

e) determine time and frequency offsets by locating peak poweroccurrences within the filtered Fourier transform.

In a particular embodiment, the above steps a) to e) for estimatingoffsets may be used effectively as a means of channel estimation. Forexample, in the above described form of the invention which providescommunication by estimating time varying channel impairments, the stepc) of updating the set of channel impairment estimates with thedetermined set of symbol estimates and received packet may comprise theabove steps a) to e) for estimating offsets.

In a further embodiment, the above method may be used as the channelestimator as required herein, in as much as updating the set of channelestimates with the determined set of symbol estimates.

In yet a further form the present invention provides a method, apparatusand system of communicating in a multiple access packet communicationnetwork by synchronizing a received signal in a multi antenna receivercomprising:

correlating a received signal observation at each of a plurality ofantennae with a known signal preamble to provide a received signalsequence;

determine a power signal of each received signal sequence;

combine the determined power signals in accordance with a time averagedweighting based on estimated antenna signal strength for each antenna;

determine a time of arrival for the received signal in accordance with apredetermined threshold condition.

In embodiments of the present invention there is provided a computerprogram product comprising:

a computer usable medium having computer readable program code andcomputer readable system code embodied on said medium for communicatingin a multiple access communication network, said computer programproduct comprising:

computer readable code within said computer usable medium for performingthe method steps as disclosed herein.

Other aspects and preferred aspects are disclosed in the specificationand/or defined in the appended claims, forming a part of the descriptionof the invention.

The present invention provides an improved or enhanced wireless linkbetween two communicating devices, for example, an IEEE 802.11a AccessPoint to an IEEE 802.11a Station or between two nodes in a wirelessmesh. The present invention leads to enhanced key performance indicatorsfor point to point links, namely, range, power, data rate andreliability. This is achieved by advanced signal processing techniquesin the following areas to improve performance

-   -   Decoding    -   Synchronisation    -   Equalisation    -   Channel Estimation    -   Full Exploitation of Multiple Receiver Antennae.

As would be understood by the person skilled in the art, in addition,techniques that exploit multiple antennas for transmission may beemployed to provide electronically generated directional antennas in anadaptive manner. The following advantages stem from the presentinvention.

-   -   Spatial rejection of interference,    -   Significantly increased receiver sensitivity,    -   Significantly increased robustness to fading, and    -   Self configuration of antenna patterns

Spatial rejection of interference effectively ignores or rejects signalsthat are not emanating from the same location as the current or point ofinterest source. Rejecting these signals increases the probability thata signal may be received without errors thus increasing the reliabilityof the link and therefore the throughput to lower retransmissions anddropped packets. Interferers have a spatial signature as measured at thereceive antenna that is substantially determined by their position.However, it is possible that transmitters that are not collocated couldproduce a similar spatial signature and it is also possible thatcollocated transmitters could produce different spatial signatures.

Significantly, increasing the receiver sensitivity means that thereceiver may operate a lower SNR (Signal-to-Noise-Ratio) point whichproduces many benefits. Since the received power at which the signal maybe successfully decoded has been reduced, the path loss may be increasedby increasing the distance between the receiver and transmitter therebyincreasing the range. Alternatively, the present invention allows thetransmit power to be decreased and still a link may be maintained.Increasing the receiver sensitivity also means that less power isrequired per bit and accordingly, it may be possible to transmit ahigher number of information bits per constellation symbol. Thisincreases the data rate.

Robustness to fading provided by the inventive techniques disclosedherein may decrease the amount of packet errors due to extreme radiochannel variations or fades. By increasing robustness, a more reliablelink may be created ensuring a better user experience and increasedthroughput through less re-transmissions and fewer dropped packets.

The use of multiple antennas for transmit and receive functions allowsthe rejection of interference from outside the direction of interest.This functionality is adaptive so no hands-on antenna orientation isrequired at install-time or during the life of the installations.

By way of example, indicative performance measures of a samplecommunications link are given with and without the use of thePoint-to-Point technology of the present invention.

Typical of Related Art Present Invention Range 300 m 1 km Required T_(x)Power 1.0 W 0.1 W Maximum Data Rate 500 Kbps 5 Mbps

The present invention also provides improved channel trackingcapabilities. Channel tracking technology refers to the adaptation ofthe receiver, when the channel changes rapidly over the duration of asingle packet. Typically, the channel estimate that is used to decode areceived packet is determined from known sequences at the start of apacket. This estimate may be used to decode the whole packet. However,if the relative speed between the transmitter and receiver is greatenough, the channel experienced at the beginning of the packet issubstantially different from that at the end of a packet rendering thechannel estimate incorrect for the end of the packet resulting indecoding errors. There are other processes that manifest themselves asthe radio channel changing over the packet. These include mismatchesbetween the Transmit and Receive Radio processing resulting in residualfrequency offsets and misalignments in the time and frequencysynchronisation. It is difficult to build transmit and receive radiodevices that match perfectly.

The advanced signal processing techniques of the present inventionallows a receiver circuit to build a progressive Channel Estimate thattracks the changes in the channel over the duration of a packet. Thebenefit of applying such Channel Tracking technology is the ability tocommunicate under high mobility conditions and under larger mismatchesbetween the transmit and receive radio processing. By way of example,typical performance measures of a sample communications link are givenwith and without the use of the Channel Tracking technology.

Typical of Related Art Present Invention Maximum Mobility 40 km/hr 400km/hr

The present invention also provides interference cancelling allowing theremoval of same standard interference from a signal. The term “samestandard” refers to transmissions of similar packet structures fromother users in a multiuser system, or multipath transmissions(reflections) from the same transmitter, or multiple transmit antenna inthe case of a device equipped with multiple transmit antenna. In allwireless communications systems, multiple active transmitters share thewireless medium. This sharing may be done in a coordinated attempt ininfrastructure networks by dividing the wireless medium into time andfrequency slots or in an uncoordinated attempt in an-hoc networks by allactive transmitters contesting for the right to use the medium. Bothschemes limit the use of the medium to a well defined frequency and timewhere only one user may transmit. Packet collisions occur when twotransmitters inadvertently choose to use the same frequency at the sametime. The Interference Cancelling technology includes advance signalprocessing techniques that benefit the following areas

-   -   Acquisition    -   Interference Mitigation    -   Range    -   Network Throughput    -   Reduced Control Overhead

Further benefits of the Interference Cancellation technologies of thepresent invention resolve collisions between two or more transmittersfrom the same standard transmitting at the same time on the samefrequency. This has numerous advantages. Firstly, when collisions occur,all transmitted packets are received correctly increasing throughput andreliability by decreasing retransmissions and dropping packets.Secondly, by removing the requirement that only one transmitter may usea given frequency at a given time the amount of traffic that can becarried on the medium may be increased. Moreover, this may give greaterflexibility in infrastructure design such as frequency planning and inthe case of co-located competing networks such as two IEEE 802.11networks from separate companies in adjoining offices.

In the case where the desired user and interfering users transmitaccording to different standards, the interference cancellationstructure may employ a receiver and re-transmitter for all relevantstandards. The receiver is then able to create hypotheses of theinterfering signals thereby enabling interference cancellation.

Collisions may be resolved in the Physical Layer in accordance withembodiments of the present invention. The resulting reduction in networksignaling overhead multiplies the benefits over and above the resolutionof the two colliding packets. Typical quantitative measures are adoubling of network throughput and several orders of magnitude reductionin packet loss rate as follows:

Typical of Related Art Present Invention Throughput 10 Mbps 20 Mbps

The multi-hop technology of embodiments of the present invention allowsselected (and possibly all) wireless devices to act as routers,forwarding packets from one device to another in a communicationnetwork. This means that though two devices may not receive each otherssignals, if there is a set of intermediate devices that may be linked toform a radio path between them, then they may communicate to each otherby passing their message through that intermediate set.

Depending on the particular network dynamics, the multi-hop technologymay employ dynamic route determination techniques to build and maintainthe required routing tables. Multi-hop networks provide many benefits interms of flexibility, reliability and cost of infrastructure.

Flexibility is achieved through a self forming network that requiresminimal planning. The only requirement is that no device may beisolated, in a radio range sense, from the core network. Allconfigurations meeting this criterion may be possible.

If multiple paths between devices exist in the network, dynamic routedetermination may select a new route when the current route is blockedor congestion is best avoided. Therefore if a device was to go offline,the network may rearrange its routing tables to exclude that device fromall routes and find a new path through the network thus creating arobust, self healing (and therefore more reliable network). Dynamicroute determination continuously adapts to network configuration changesallowing for mobile network nodes.

Multi-hop networks in accordance with embodiments of the presentinvention offer a simple solution to provide a high bandwidth link overa wide area. Due to easy flexible installations, low infrastructurecosts and a high rate, reliable link, multi-hop networks generally offerexcellent return on investment.

Four areas of application in the communications field which best utilizethe benefits of the technologies of embodiments of the present inventionhave been identified by the inventor as

-   -   Mobile Multi-hop Radio Networks    -   Fixed Multi-hop Radio Networks    -   IEEE 802.11a Access Point Chipsets    -   802.16 Base Stations    -   OFDM Baseband Receiver Co-processor

The following describes each of the above identified applications inturn. Other applications may also benefit from these technologies ofembodiments of the present invention.

Firstly, a Mobile Multi-hop Radio Network requires effective real-timecommunication to networks of moving entities. This concept providescost-effective bi-directional high bandwidth communication both betweenthe mobile entities and between fixed networks and the mobile entities.Wireless Routers are placed where service is required with regularconnections to a wideband backbone network. A fixed network may be usedto connect to other networks such as the internet or other privatenetworks. Other than access to power and a physical mounting point noother infrastructure is required for each wireless router. The wirelessrouters may be fixed or mobile. The routers adapt to their environmentin terms of link quality using, for example, data communications methodsas would be understood by the person skilled in the art. Embodiments ofthe present invention provide a competitive advantage relative to otherMulti-hop Radio Networks in that the improved mobility and range, asnoted above, leading to a more efficient network is provided. Relativeto related art Private Communications Networks, embodiments of thepresent invention provide significant improvements in Data Rate, Range,Mobility and cost of Network as noted above.

Secondly, a Fixed Multi-hop Radio Networks is provided by installingWireless Routers at fixed user locations with links available to one ormore wideband backbone connections. The only requirement is that allrouters must be able to form a link (direct or hopped) back to abackbone connection. There is no need for expensive base stationconfigurations and ultimate range is not limited by signal strength. TheFixed Multi-hop radio Network forms a flexible, low infrastructure costsolution in providing a high bandwidth connection to a Wide Area Networkthat is reliable, easily managed and self healing.

Furthermore, the present invention enables all decoder outcomes to beemployed (decoder outcomes are stored across all iterations and able tobe combined) in the receiver filter structure providing improvedestimate determination. The number of users that may be supported isgreatly increased. Particularly advantageous, for example, in OFDMsystems the present invention does not require prohibitively largematrices to be inverted in forming estimates. Receiver performance issuperior to that of the related art due to the quality of the feedbacksymbol provided by including decoding in the iteration loop. Embodimentsof the present invention are based on interference cancellation whereprevious estimates of the multi user received signals are subtractedfrom the received signal to cancel the interference they cause.Accordingly, these embodiments do not suffer the disadvantages andcomplexities of using tree search methodologies for multiuser signalswhich would necessitate exploring many paths through a given tree. Thepresent invention advantageously enables decoding of each user's signalaccording to their Forward Error Correction encoding. This use of strongerror control code structure provides for significantly improved symbolestimates, resulting in superior interference estimates. This in turnallows support for significantly higher numbers of users. Embodiments ofthe present invention do not require synchronised users to enableimproved multi user reception. Embodiments of the present inventionadvantageously use decoder outcomes as training symbols rather than onlyusing demodulator outcomes. Advantageously, receiver coefficients forbeamforming may be determined without transmitter interaction. Also theuse of decoder outcomes to improve channel estimates allows accurateestimation of the required beamforming coefficients. In accordance withembodiments of the present invention, smoothing of channel estimate tapsis performed in the frequency domain as well as the time domain. Furtherto this, embodiments of the present invention allow decoding of symbolsmore than once as a channel estimate corresponding to its interval isimproved resulting in increased receiver sensitivity.

Further scope of applicability of the present invention will becomeapparent from the detailed description given hereinafter. However, itshould be understood that the detailed description and specificexamples, while indicating preferred embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Further disclosure, improvements, advantages, features and aspects ofthe present invention may be better understood by those skilled in therelevant art by reference to the following description of preferredembodiments taken in conjunction with the accompanying drawings, whichare given by way of illustration only, and thus are not limiting to thescope of the present invention, and in which:

FIG. 1 illustrates a related art multiple access wireless communicationsystem;

FIG. 2 illustrates an example of a MAC failure in a related wirelesscommunication system involving an access collision;

FIG. 3 depicts self interference in WLAN network of a related artwireless communication system;

FIG. 4 depicts a generic iterative receiver structure in accordance witha first embodiment;

FIG. 5 depicts the transmission system model for coded CDMA;

FIG. 6 depicts a canonical iterative multiuser decoder;

FIG. 7 depicts an iterative multiuser decoder with linear multiuserestimation in accordance with a first embodiment;

FIG. 8 depicts the recursive filter Λ_(k) ^((n)) in accordance with afirst embodiment for n=1 the input signal is r while for n≧ the inputsignal is {circumflex over (x)}_(k) ^((n-1)); and

FIG. 9 depicts Bit Error Rate versus users after 10 iterations, N=8,E_(b)/N₀=5 dB in accordance with a first embodiment;

FIG. 10 shows a typical related art single pass OFDM receiver high levelstructure;

FIG. 11 illustrates an adaptation of the single pass OFDM receiver highlevel structure of FIG. 10 in accordance with a second embodiment tofacilitate iterative receiver technologies;

FIG. 12 shows a OFDM Soft/Hard Decode and Re-transmit structure for usein Iterative Receive structure in accordance with a second embodiment;

FIG. 13 shows a Hybrid Re-transmit in accordance with a secondembodiment;

FIG. 14 shows a Hard Decode and Re-Modulate for OFDM Soft/Hard Decodeand Re-transmit structure in accordance with a second embodiment;

FIG. 15 shows a Soft Decode and Re-Modulate for OFDM Soft/Hard Decodeand Re-transmit structure in accordance with a second embodiment;

FIG. 16 shows a structure for time domain channel application process inaccordance with a second embodiment;

FIG. 17 shows a structure for frequency domain channel applicationprocess in accordance with a second embodiment; and

FIG. 18 shows an Example of a Typical OFDM Packet Physical layer Formatand an associated Multiplexer mapping;

FIGS. 19 a and 19 b show a wireless modem incorporating a basebandreceiver processor in accordance with preferred embodiments of thepresent invention;

FIG. 20 illustrates a packet structure in accordance with related art;

FIG. 21 illustrates an example related art time synchronisationdecision;

FIG. 22 shows triangle filter coefficients for a receiver filter inaccordance with a third embodiment of the invention;

FIG. 23 shows an example of a filtered decision statistic in accordancewith a third embodiment of the invention;

FIG. 24 represents an actual frequency domain of a related art radiochannel;

FIG. 25 represents the frequency domain of FIG. 24 after receiver phaseand frequency offset correction;

FIG. 26 represents an error pattern for a related art processing of areceiver;

FIG. 27 represents a radio channel estimate after smoothing across OFDMsymbols in accordance with a fourth embodiment of the invention;

FIG. 28 represents an error pattern for a fourth embodiment of theinvention using perfect training symbols;

FIG. 29 represents a raw radio channel estimate or channel estimatedatabase in accordance with a fourth embodiment of the invention;

FIG. 30 is an example of a WLAN packet format in accordance with relatedart;

FIG. 31 is an OFDM symbol sub-carrier matrix structure in accordancewith a fifth embodiment of the invention;

FIG. 32 is a representation of channel power (amplitude) over asub-carrier and OFDM symbol resulting from application of a fifthembodiment of the invention;

FIG. 33 is a representation of channel phase corresponding to thewaveform represented in FIG. 32;

FIG. 34 is a representation of a synchronisation metric of a sub-carrierand OFDM symbol in accordance with a fifth embodiment of the invention.

DETAILED DESCRIPTION System Overview

In wireless networks a signal received at a network device comprisescomponents from all active transmitters. These components, along withnoise, add together resulting in the received signal. In some cases,only one of these components, corresponding to a specific transmitter,is of interest. In other cases, such as a reception at a network accesspoint, several of the received components are of interest. In eithercase the presence of the other signal components in the received signalinhibits the accurate estimation of any given transmitted signal ofinterest. In accordance with embodiments of the present invention asystem and methods and apparatus for processing a received signalcomprising one or more received signal components from differenttransmitters is disclosed herein. The processing typically resides inthe baseband receiver processing of a wireless transceiver 190 asillustrated in FIGS. 19 a and 19 b. The Radio Frequency TransceiverIntegrated Circuit (IC) is an analogue device that interfaces betweenthe digital signal processing components LLC, MAC, Rx, Tx, and theantenna system of the transceiver. In receive mode IC amplifies anddownconverts the received signal suitable for driving analogue todigital converters. In transmit mode it up converts and amplifies thesignal for excitation of the antenna.

The baseband receiver is responsible for determining the existence ofany packets and then to recover transmitted information estimates fromthe received signal if packet(s) are deemed to exist.

A canonical baseband receiver processor Rx is shown in FIG. 19 b. Thereceived signals for each antenna are supplied as input by the RadioFrequency Circuit IC. These signals are then filtered 302 by filters 302a, 302 b to remove any out of band interference. The filtered signals303 are then combined with the current Received Signal Estimates 306,implementing an interference cancellation function 304. Ideally, theinterference cancellation module 304 removes the signal components inthe received signal pertaining to all packets except for the packet ofinterest. The packet of interest is then decoded by feeding theInterference Cancelled output 309 to a Single Packet Processor 313.

The Single Packet Processor 313 takes a Multiantenna received signal asdelivered by the Interference Cancellation module 304 and produces anestimate of the transmitted information bits 314 and an estimate of thereceived symbols 306 for the packet of interest. These symbols, alongwith the channel estimates for the packet of interest, are then fed backto the interference cancellation module 304. In some cases it ispreferred to send back only the transmitted symbol estimates to theinterference cancellation module 304.

The Single Packet Processor 313 may contain advanced or conventionalsingle packet techniques. The multiuser interference rejectionperformance of the receiver will be better if the Single PacketProcessor is of high quality. Techniques pertaining to synchronisationand channel estimation are key to the performance of the Single PacketProcessor 313.

Techniques that improve the robustness of the synchronisation andchannel estimation employed in decoder 310 are described herein. Thesynchronisation uses all antenna signals in its operation. The channelestimation makes use of the decoder outcomes to improve the channelestimation accuracy.

New packets are found by a searcher in the interference cancellationmodule 304. The searcher investigates an intermediate signal generatedin the module 304. This intermediate signal is the received signal minusthe estimated received signal for all currently detected packets and isreferred to as a noise hypothesis since in ideal conditions alltransmitter components are removed from the received signal leavingbehind only the random noise.

In applications sensitive to latency the feedback loops, both inside 310for decoder outcome assisted channel estimation, and between 304, 310and 312 for multi packet interference cancellation may be executed at arate higher than the packet rate. In OFDM based systems the preferredchoice for the loop rates is the OFDM symbol rate with decoding andinterference cancellation occurring at the OFDM symbol rate.

In applications where packet based decoding and interferencecancellation may be performed at the packet rate additional packet-basedtechniques for the Single Packet Processor 313 are disclosed. Thesetechniques leverage the extra signal processing gain available whenconsidering long sequences of symbols.

In either case, lists of current estimates of the quantities passedbetween the Interference Canceller 304 and the Single Packet Processor313 are required. A controller determining which packet is to be updatedmay also be utilised.

With reference to FIGS. 4 to 9, a first embodiment stems from thegeneral realization that over a number of iterations using linearfilters in a multiuser receiver, each iteration provides new informationand, as the filter structure converges, the output of the decoders alsoconverges and eventually becomes completely correlated. The linearfilters of the multiuser decoding circuit means may be structured inaccordance with at least one predetermined recursive expression.

An innovation in the filter design of a first embodiment disclosedherein is to exploit the fact that information provided by the decodersis initially only marginally correlated over iterations, i.e. in thefirst few iterations, each iteration provides new information. As thestructure converges, the output of the decoders also converges andeventually becomes completely correlated.

The disclosed filter design is based on a technique to use all availableinformation from all previous iterations. This implies that the filtergrows linearly in size by a factor equal to the number of users. This isclearly impractical. Thus, the disclosed filter design makes it possibleto use all the available information through recursive feedback of thefilter output over iterations, without requiring a growing filter. Thesize of the filter remains the same. In order to achieve this, thefilters in the structure may be designed according to the recursiveexpressions derived herein.

Related structures, having lower complexity implementations, areobtained by modifying the specific filters used in the structure. Thegeneral recursive structure, however, is still fundamental for suchmodified filters. In these cases, the individual filters are designedaccording to appropriately different strategies using the principlesdisclosed herein.

The recursive filtering structure for iterative signal processingdisclosed herein is not limited to multiuser detection, but may also bedirectly applied within systems and functionalities of the samestructure. Examples of such applications are iterative equalisation,iterative joint channel estimation and detection/decoding, iterativespace-time processing, and iterative demodulation.

In a broad aspect of the first embodiment, an iterative signalprocessing arrangement shown generally in FIG. 4 as 10 having one ormore pairs of first and second signal processing components 1, 2, thepairs of components being in iterative configuration, each of the firstsignal processing components having as input one or more receivedsignals dependent upon one or more transmitted signals, wherein for eachsaid signal processing component pair the output of said first signalprocessing component 1 is an estimate of a characteristic of a selectedtransmitted signal based on the current and one or more previous signalsreceived by said first signal processing component 1, which is input tosaid corresponding second signal processing component 2 that provides afurther estimate of said selected transmitted signal to the output ofsaid second signal processing component 2, the outputs of all saidsecond signal processing components of respective pairs are input toeach said first signal processing components of all said pairs in asucceeding iteration cycle.

In a further aspect of the first embodiment, the iterative signalprocessing arrangement 10 according to that described above wherein saidfirst signal processing component 1 comprises at least two lineariterative filters wherein a first of said linear iterative filtersoutputs an estimate of a selected-characteristic of a selected one orsaid transmitted signals to said second signal processing component 2,and a second of said iterative filters having the same inputs as saidfirst linear iterative filter provides an estimate of a characteristicof a selected of one or more transmitted signals and then delays by oneiteration cycle said estimate and outputs said delayed estimate to aninput of said first linear iterative filter.

This first embodiment is intended for application to any communicationsystem described by a generic linear channel model. The received signalat the input to the receiver is described by a weighted sum of thetransmitted signals plus noise. The set of weighting factors representsa set of linear constraints imposed on the transmitted signals. Otherconstraints could possibly have been imposed on the signals. These otherconstraints are independent of the linear constraints imposed by thelinear channel.

The optimal receiver structure finds the estimates of the transmittedsignals, subject to all the imposed constraints. This approach isprohibitively complex for most practical cases of interest. As analternative, a generic iterative receiver structure comprises of twoseparate components (see FIG. 4). The first component 1 finds theoptimal estimates, only subject to the linear channel constraints,ignoring all other constraints. Only preferably these estimates areshuffled by reordering according to a pre-determined order(de-interleaved) and used as inputs to the second component 2 whichfinds the optimal estimates subject only to all the other constraints,ignoring the linear channel constraints. These estimates are in turn,preferably shuffled back into the original order (interleaved), undoingthe pre-determined reordering, and used as inputs to the first component1 in the succeeding iteration cycle.

The optimal design of the first component 1, enforcing the linearchannel constraints is often also prohibitively complex. To limitcomplexity, the component design itself can be constrained to be linear,leading to a linear signal processing component. The design of thislinear signal processing component, given selected inputs, is the mainsubject of this disclosure with respect to the first embodiment. For thefollowing description, the first embodiment lies in the linear signalprocessing component, or signal processing component 1, corresponding tocomponent 1 in FIG. 4. The remaining part of FIG. 4 is referred to assignal processing component 2.

The function of the linear signal processing component 1 is to separatea selected transmitted signal from other “interfering” transmittedsignals, based on the received signal which is a weighted sum of alltransmitted signal as described above.

The input to the linear signal processing component 1 are one or morereceived signals and one or more estimates of the transmitted signals,provided by signal processing component 2. The output of the linearsignal processing component 1 is an estimate of the selected transmittedsignal.

The linear signal processing component 1 comprises two linear filters.The first filter provides as output estimates of the selectedtransmitted signal based on inputs of one or more of the input signalsto the linear signal processing component, the output of this firstfilter delayed by one processing time period of the iterative cycle, andthe output of the second filter delayed by one processing time period ofthe iterative cycle.

The second filter provides as output estimates of one or more of theother transmitted signals (interfering with the selected transmittedsignal) based on inputs of one or more of the input signals to thelinear signal processing component, and the output of the second filterdelayed by one processing time period of the iterative cycle.

The output of the first filter is the output of the linear signalprocessing component.

Specific embodiments of the first embodiment will now be described insome further detail with reference to and as illustrated in theaccompanying figures. These embodiments are illustrative, and not meantto be restrictive of the scope of the embodiment. Suggestions anddescriptions of other embodiments may be included but they may not beillustrated in the accompanying figures or alternatively features of theembodiment may be shown in the figures but not described in thespecification.

This embodiment is described using linear multiuser estimators (MUEs)suitable for use as part of an iterative multiuser decoder. A specificapplication of the technique in the field of turbo-decoding in atransmission system for coded CDMA is provided. However, as statedpreviously the structure of the filter and the principles revealed areuseful in many other areas of the communications field. Thus theembodiment provided should not be considered as limiting in any way.

The specification includes theoretical considerations expressed in anappropriately precise fashion and uses mathematical analysis to provethe correctness of the approach using assumptions as required. Not allproofs of theorems used are provided herein. A disclosure such as thatcontained herein has directed correlation to practical devices andconfigurations of filter elements of performing the functions described.Furthermore the disclosure provided herein would be readily understoodby those skilled in the art. The disclosure is such that a personskilled in the art can readily translate the theoretical configurationsof elements disclosed herein into a variety of devices to solve problemsor improve the performance of devices and algorithm in a variety ofapplication areas some of which have been described previously and thatwill be described herein.

This embodiment is intended for application to any communication systemdescribed by a generic linear channel model. The received signal at theinput to the receiver is described by a weighted sum of the transmittedsignals plus noise. There could be multiple received observablespertaining to the same symbol internal, ie, the received signal can be avector of received observables,

$\begin{matrix}{r = {{\sum\limits_{i - 1}^{K}{s_{i}x_{i}}} + n}} & (1)\end{matrix}$

where a total K signals are transmitted, s_(k) is the weighting factorsfor signal x_(k) and n is a noise vector.

Here, the set of weighting factors, s₁, s₂, . . . , s_(K) represents aset of linear constraints imposed on the transmitted signals. Otherconstraints could possibly have been imposed on the signals x₁, x₂, . .. , x_(K) such as error control encoding, channel fading etc. Theseother constraints are independent from the linear constraints imposed bythe linear channel.

The optimal receiver structure finds the estimates of the transmittedsignals, subject to all the imposed constraints. This approach isprohibitively complex for most practical cases of interest. As analternative, a generic iterative receiver structure comprises of twoseparate components (see FIG. 4). The first component 1 finds theoptimal estimates, only subject to the linear channel constraints,ignoring all other constraints. These estimates are inputs to the secondcomponent 2 which finds the optimal estimates subject only to all theother constraints, ignoring the linear channel constraints. Theseestimates are in turn, provided as inputs to the first component 1 inthe following iteration cycle.

The optimal design of the first component 1, enforcing the linearchannel constraints is often also prohibitively complex. To limitcomplexity, the component 1 design itself can be constrained to belinear, leading to a linear filter. The design of this linear filter,given selected inputs to the filter, is disclosed herein. The functionof the filter is to separate a selected signal from other “interfering”signals, based on the received signal which is a weighted sum of alltransmitted signal as described in (1). All the references provided inthis specification are incorporated herein by reference and for allpurposes. An innovation in the filter design disclosed herein is toexploit the fact that information provided by the decoders is initiallyonly marginally correlated over iterations, i.e., in the first fewiterations, each iteration provides new information. The disclosedfilter design is based on a technique to use all available informationfrom all previous iterations.

This implies that the filter grows linearly in size by a factor equal tothe number of users. This is clearly impractical. Thus, the disclosedfilter design makes it possible to use all the available informationthrough recursive feedback of the filter output over iterations, withoutrequiring a growing filter. The size of the filter remains the same. Thefilter design is based on two linear iterative filters, where the firstlinear filter provides an estimate of the desired signal based on thereceived signal, the most current estimates of all user signals fromsignal processing component 2, and the output of the second linearfilter which is a vector of estimates of all user signals based on allprevious inputs to signal processing component 1. The two linear filtersare shown explicitly in FIG. 8.

The linear iterative filters may appropriately be designed based on thelinear minimum mean squared error criterion, according to the recursiveexpressions derived therein.

This embodiment applies to any system described by such a generic linearchannel model, and where an iterative receiver as described above, is tobe applied. Examples of such applications include (but are not limitedto) the following:

-   -   Decoding of coded transmission in a linear multiple access        system.    -   Decoding of coded transmission over an inter-symbol interference        channel.    -   Joint channel estimation and detection/decoding of coded        transmission over unknown channels.    -   Decoding of space-time coded transmission.    -   Decoding of coded transmission with higher order modulation        formats.

In the following, the design is demonstrated for multiuser decoding fora general linear multiple access system.

System Model in Multiuser Decoding Example

The basic principle behind turbo decoding is to decode independentlywith respect to the various constraints imposed on the received signal.The overall constraint is accommodated by iteratively passing extrinsicinformation between the individual decoders. For turbo codes, theseconstraints are the parallel concatenated codes. For turbo-equalisationthey are the channel code and the memory of the inter-symbolinterference channel. For multiuser decoding, there are constraints dueto the multiple-access channel and due to the individual users'encoders.

In this embodiment, a theoretical framework for the derivation of linearmultiuser estimators (MUEs) suitable for use as part of an iterativemultiuser decoder is disclosed. We consider a two-input linear minimummean squared error (LMMSE) estimator which inspires our main result, thederivation of a recursive Bayesian estimator. The proposed estimatoryields estimates based on the received signal and all the successiveoutputs provided by the error control code decoders over all previousiterations. This approach is motivated by an observation that theseestimates are loosely correlated during initial iterations.

Notation: P^(n) is the space of probability n-vectors (length nnon-negative vectors that sum to 1). For random vectors x and y, E[x] isthe expectation, varx=E[x*x] and covx=<x,x>=E[xx*]. Likewisecov(x,y)=<x,y>=E[xy*].

We consider the K-use linear multiple-access system of FIG. 5. User k,k=1.2, . . . , K encodes its binary information sequence b_(k)[l] usinga rate R code C, to produce the coded binary sequence d_(k)[l].

Consider transmission of 2L code bits per user. Each user independentlypermutes their encoded sequence with an interleaver π_(k). Denote thesequence output from the interleaver of user k as u_(k)[l], l=1, 2, . .. 2L. Pairs of interleaved code bits u_(k)[l] are memorylessly mappedonto the quaternary phase-shift keyed (QPSK) signal constellation,Q={±1/√{right arrow over (2)}±j/√{right arrow over (2)}}, givingsequences of modulated code symbols x_(k)[i], where i=1, 2, . . . , L isthe symbol time index. We choose QPSK only for simplicity and note thatdifferent code constraints and symbol maps across users are possible ingeneral.

At symbol time i, each user transmits s_(k)[i]x_(k)[i], themultiplication of x_(k)[i] with the real N-chip spreading sequence,s_(k)[i]ε{−1, 1}^(N). We model the use of spreading sequences withperiod much longer than the data symbol duration by letting each elementof s_(k)[i] be independent and identical distributed over users andtime. For conceptual ease only, users are symbol synchronised, transmitover an additive white Gaussian noise (AWGN) channel, and are receivedat the same power level. These assumptions however are not required.Write the chip-match filtered received vector r[i]ε□^(N) at symbol timei=1, 2, . . . , L as

r[i]=s[i]×[i]+n[i]  (2)

where S[i]=(s_(i)[i], s₂[i], . . . , s_(k)[i]), is a N×K matrix with thespreading sequence for user k as column k. The symbol □ represents theset of complex numbers. The vector x[i]εQ^(K) has elements x_(k)[i] andthe vector n[i]ε□^(N) is a sampled circularly symmetric i.i.d. Gaussiannoise process, with covn[i]=σ²I. The symbol Q represents the set ofpossible modulated symbols, e.g. QPSK.

Henceforth, it is not required to identify specific symbol intervals andthese indices will be omitted. For later use, we define S _(k) =(s₁, s₂,. . . , s_(k−1), s_(k+1), . . . , s_(k)) and x _(k) =(x₁, x₂, . . . ,s_(k−1), k_(x+1), . . . , x_(K))^(t) to indicate deletion of user k fromS or x.

Recursive Filter from Multiuser Estimation

Application of the turbo-principle to the coded linear multiple-accesssystem, where for each user, we treat the error control code as oneconstraint and the multiuser channel (2) as the other constraint,results in the canonical receiver structure of FIG. 6[1].

An iteration n₁, the multiuser APP takes an input r and the set ofextrinsic probabilities q_(k) ^((n−1)) from user k=1, 2, . . . , Kcalculated in the previous iteration n−1. q_(k) ^((n−1))[i]εP^(|Q|) isthe extrinsic probability distribution on the transmitted symbolsx_(k)[i]εQ of user k. The set Q is the set of all possible modulatedsymbols at the transmitter. The multiuser APP calculates the updatedextrinsic probability vector p_(k) ^((n))[i] for user k. Afterappropriate de-interleaving, the extrinsics p_(k) ^((n)) are used aspriors for independent APP decoding of the code C by each user,producing (after interleaving) the extrinsics q_(k) ^((n)) which serveas priors for the subsequent iteration. The marginalisation in themultiuser APP requires summation over |Q|^(K−1) terms. Manylower-complexity alternatives have been proposed while retaining thesame basic architecture.

Consider the receiver structure shown in FIG. 7. There is a bank oflinear filters Λ_(k) ^((n)), one for each user. The coefficients ofthese filter may be re-computed every iteration. For the firstiteration, n=1, the input to Λ_(k) ⁽¹⁾ is just r. For subsequentiterations n=2, 3, . . . , the input to the filter for user k is r and aset of signal estimates for all the other users from previousiterations, {{circumflex over (x)}_(K′) ^((M)):k′≠k,mεM}, where M⊂{1, 2,. . . , n−1} is a set defining the memory order of the iteration.Typically in the literature, M={n−1}, although recently M={n−1,n−2} hasbeen considered [2].

The output of the filter Λ_(k) ^((n)) is an updated sequence ofestimates {circumflex over (x)}_(k) ^((n)) of the corresponding codesymbol for user k. These estimates are mapped from the signal space ontothe probability vector space using a symbol-wise mapping T:□→P^(|Q|).The resulting sequence of probability vectors p_(k) ^((n)) are used aspriors for individual APP decoding of the code C. These APP decoders canoutput either posterior or extrinsic probabilities q_(k) ^((n)) (bothapproaches have been investigated in the literature). The sequence ofprobability vectors q_(k) ^((n)) is in turn mapped back onto the signalspace by a symbol-wise function U:P^(|Q|)→□. Typically, T calculates thevectors p_(k) ^((n)) assuming that {circumflex over (x)}_(k) ^((n)) isGaussian distributed with known mean and variance, {circumflex over(x)}_(k) ^((n)): N({tilde over (μ)}_(k) ^((n)),

_(k) ^((n))). Likewise, a common choice for U is the conditional mean.

The following easily proved lemma provides a useful general frameworkfor the derivation of filters Λ_(k) ^((n)).

Lemma 1

Suppose that for a parameter x we have the vector observationc=(a^(t)b^(t))^(t), the concatenation of two vector observations a andb. The LSE estimate of x

{tilde over (x)}=

x,a

a,a

⁻¹ a+m(b−

b,a

a,a

⁻¹ a) given c is  (3)

where

m=x,b>−<x,a><a,a> ⁻¹ <a,b>)(<b,b>−<b,a><a,a> ⁻¹ <a,b>)⁻¹

We see that (3) can be written as {tilde over (x)}=ga+m(Fa−b), where

m=(<x,b>−<x,a><a,a> ⁻¹ <a,b>)(<b,b>−<b,a><a,a> ⁻¹ <a,b>)⁻¹  (4)

F=<b,a><a,a> ⁻¹  (5)

g=<x,a><a,a> ⁻¹  (6)

So far in the literature, linear filters Λ_(k) ^((n)) for multiuserestimation in iterative decoding have been designed based on thereceived signal r and the most current code symbol estimates of theinterfering users {circumflex over (x)}_(k) ^((n)). After n iterations,we have however a sequence of such estimates available, namely{{circumflex over (x)}_(k) ⁽¹⁾, {tilde over (x)}_(k) ⁽²⁾, . . . {tildeover (x)}_(k) ^((n))} together with r. It has been observed that theestimates are not strongly correlated during the initial iterations [2].

Consider the following recursively defined version of observables asinput to the filter Λ_(k) ^((n)),

$\begin{matrix}{c_{k}^{(n)} = \left\{ \begin{matrix}r & {n = 1} \\\begin{pmatrix}c_{k}^{({n - 1})} \\{\hat{x}}_{k}^{({n - 1})}\end{pmatrix} & {{n = 2},3,\ldots}\end{matrix} \right.} & (7)\end{matrix}$

Direct application of the LMMSE criterion results in Λ_(k)^((n))=<x_(k),c_(k) ^((n))><c_(k) ^((n)),c_(k) ^((n))>⁻¹. It is clearhowever that Λ_(k) ^((n)) grows in dimension with 12 which isimpractical.

Inspired by recursive Bayesian estimation (RBE) [3], we can prove thefollowing theorem that solves this dimensionality problem by giving arecursive form from Λ_(k) ^((n)) (subject to certain constraints on theinput signal).

Theorem 1

Make the following assumptions,

A1: The received signal r=Sx+n, is described according to (2) where n iscircularly symmetric complex Gaussian with covn=σ²I, and σ² and s areknown.

A2: The interleaved code symbol estimates of the interfering users{circumflex over (x)} _(k) ^((n)) coming out of the single user APPdecoders can be written as {circumflex over (x)}_(k) ^((n))=x_(k)^((n))+{circumflex over (v)}_(k) ^((n)) where {circumflex over (v)}_(k)^((n)) is uncorrelated with x and also uncorrelated over time anditerations, but not over users at a given iteration, i.e. <x,{circumflexover (v)}_(k) ^((n))>=0,<{circumflex over (v)}_(k) ^((n)),{circumflexover (v)}_(k) ^((m))>=0 for n≠m and <{circumflex over (v)}_(k)^((n)),{circumflex over (v)}_(j) ^((n))>=a_(kj).

Define Q_(k) ^((n))=<{circumflex over (v)} _(k) ^((n)), {circumflex over(v)} _(k) ^((n))>, with elements determined as shown above.

Let c_(k) ^((n)) be according to (7). Under A1 and A2 the LMMSE estimateof x_(k) given c_(k) ^((n)) is given by the output {tilde over (x)}_(k)^((n)) of the recursive filter shown in FIG. 8.

The update for the estimate is

{tilde over (x)} _(k) ^((n)) ={tilde over (x)} _(k) ^((n−1)) +m _(k)^((n))({circumflex over (x)} _(k) ^((n−1)) −{tilde over (x)} _(k)^((n−1)))

The filters in the figure are defined as follows:

m _(k) ^((n)) =−w _(k) ^((n))(I+Q _(k) ^((n−1)) −W _(k) ^((n)))⁻¹

M _(k) ^((n))=(I−W _(k) ^((n)))(I+Q _(k) ^((n−1)) −W _(k) ^((n)))⁻¹

with the recursive update equations for n=3, 4, . . . .

w _(k) ^((n)) =w _(k) ^((n−1)) [I−(H_(k) ^((n−1)))⁻¹(I−W _(k)^((n−1)))]⁻¹

W _(k) ^((n)) =W _(k) ^((n−1))+(I−W _(k) ^((n−1)))H _(k) ^((n−1)))⁻¹(I−W_(k) ^((n−1)))

H _(k) ^((n−1)) −I+Q _(k) ^((n−2)) −W _(k) ^((n−1))

The initial conditions with {tilde over (x)}_(k) ⁽⁰⁾=0 and x _(k) ⁽⁰⁾=0are m_(k) ⁽¹⁾=s_(k) ^(t)(SS^(t)+σ²I)⁻¹, M_(k) ⁽¹⁾=S _(k)^(t)(SS^(t)+σ²I)⁻¹ for n=1 and w_(k) ⁽²⁾=s_(k) ^(t)(SS^(t)+I)⁻¹S _(k) ,W_(k) ⁽²⁾=S _(k) ^(t)(SS^(t)+σ²I)⁻¹S _(k) for n=2.

Computer simulations have been used to evaluate the proposed technique.For the purposes of simulation, each user used the maximum free distance4 state convolutional code naturally mapped onto QPSK. Each user istherefore transmitting 1 bit per channel use. Binary spreading sequenceswith N=8 were generated i.i.d. at each symbol for each user.Transmission is chip synchronous and all users are received at the samepower level.

Indicative simulation results are shown in FIG. 9. Three curves areshown. PIC is the parallel interference cancellation method of [4]. IPICis the improved parallel interference cancellation of [2]. RBE is theproposed recursive Bayesian estimation technique. Each of the curvesbegins for small numbers of users at the single-user BER near 10⁻⁴. Aseach receiver fails to converge, its curve deviates from single-user.For PIC, this occurs at K/N=1.125. For IPIC, the limit is 1.625 and forRBE 1.875. The performance benefit of IPIC over PIC is reported in [2].The recursive Bayesian technique supports even higher loads. In fact,further numerical investigations (for smaller systems) have shown thatRBE supports almost the same load as using the multiuser APP.

Described herein is a computationally efficient recursive filter for usein iterative multiuser decoding. This filter uses the entire history ofoutputs from the single user decoders in order to accelerate convergenceand to support greater loads.

With reference to FIGS. 10 to 18 a second embodiment is described wherethere are a number of specific solutions offered which fall out from thegeneral solution of (or realization that) adapting related art singlepass OFDM receivers to iteratively receive signals at the sampling levelallows the receiver to differentiate a desired packet from anobservation of an interference (collision) signal at the receiver input.These solutions are as follows:

-   -   An overall system solution—Iterative Receiver Structure itself.    -   Additional solution aspect—Samples Estimates list.    -   Additional solution aspect—Information Bit Estimates list.    -   Additional solution aspect—Multiplexing of Time/Frequency Domain        Channel Application Sample Estimates.

In one aspect, the second embodiment provides a system and method ofreceiving OFDM packets comprising the following:

a) sample a receiver input signal consisting of signals from one or moreantenna;

b) add the input signal with one of a plurality of prior stored receivedpacket sample estimates to determine a packet sample hypothesis;

c) determine an information bit estimate from the sample hypothesis forstorage in an information bit estimates list;

d) determine an updated received packet sample estimate from the samplehypothesis for updating the plurality of prior stored estimates;

e) subtract the updated sample estimate from the sample hypothesis todetermine a noise hypothesis and provide the noise hypothesis as thereceiver input signal;

f) repeat steps a) to e) until at least one or more complete packets areaccumulated in the information bit estimates list.

In another aspect, the second embodiment provides a system and method ofproviding a sample estimates list in an OFDM receiver comprising thefollowing:

a) sample a receiver input signal;

b) determine a packet sample estimate from the sampled receiver inputsignal;

c) store the packet sample estimate;

d) determine a packet sample hypothesis by adding the receiver inputwith a selected previously stored packet sample estimate;

e) determine an updated packet sample estimate by decoding andre-transmission modelling the packet sample hypothesis;

f) update the selected previously stored packet sample estimate with theupdated packet sample estimate.

In yet another aspect the second embodiment provides a system and methodof providing a packet information bit estimates list in an OFDM receivercomprising the following:

a) determine a packet sample hypothesis by adding a receiver input witha selected previously stored packet sample estimate;

b) determine an information bit estimate by decoding the packet samplehypothesis with one or more of a hard decoding technique and a softdecoding technique

c) storing the information bit estimate with one or more previouslydetermined information bit estimates;

d) repeating steps a) to c) until a complete packet is accumulated.

In still another aspect, the second embodiment provides a system andmethod of determining a hybrid OFDM received packet sample estimatecomprising the step of:

multiplexing a time domain channel application received sample estimatewith a frequency domain channel application received sample estimate,such that the multiplexed time domain sample estimate is mapped tocorrespond to one or more of;

an OFDM signal cyclic prefix;

an OFDM tail portion, and;

an OFDM guard period,

wherein the multiplexed frequency domain sample estimate is mapped tocorrespond to one or more of;

an OFDM signal preamble and;

an OFDM payload data symbol.

In another aspect the second embodiment provides an iterative sampleestimation method for OFDM packet based network communication comprisingthe following steps:

a) selecting either the windowed matched received samples or the noisehypothesis as the input signal;

b) adding an empty packet estimate to a samples estimate list containingpacket sample estimates;

c) selecting one of said list entries;

d) adding said packet samples estimate to said input signal to create apacket received samples hypothesis;

e) decoding and re-transmission modelling of said packet receivedsamples hypothesis to create a new packet received samples estimate andnew information bit estimates;

f) updating said information bit estimate list with new information bitestimates;

g) subtracting said new packet samples estimate from said packetreceived samples hypothesis to create a noise hypothesis; and

h) updating said samples estimate list entry with said new packetsamples estimate;

all said steps being iterated at least once for each packet.

In a further aspect the second embodiment provides an iterative sampleestimation method according to the previous paragraph wherein step e)further comprises:

i) soft decoding said selected packet sample estimate to create softencoded bits and new packet information bit estimates for reinsertioninto said information bit estimates list;

j) soft modulating said soft encoded bits to create a transmitted symbolestimate;

k) constructing the time domain channel estimate from said packetreceived samples hypothesis and said transmitted symbol estimates;

l) constructing the packet transmit sample estimate from saidtransmitted symbol estimate;

m) convolving said time packet transmit sample estimate with said timedomain channel estimate to create the time domain channel appliedreceived samples estimate; and in parallel with steps k) and m);

n) constructing the frequency domain channel estimate from said packetreceived samples hypothesis and said transmitted symbol estimates;

o) multiplying said frequency domain channel estimate with saidtransmitted symbol estimates to create packet received symbol estimates;then

p) constructing the frequency domain channel applied received samplesestimate from the packet received symbol estimates; and

q) multiplexing the time domain channel applied received samplesestimate with the frequency domain channel applied received samplesestimate for reinsertion into said samples estimate list, wherein stepsn) to p) are repeated for each OFDM symbol in a packet.

In still another aspect, the second embodiment provides an iterativesample estimation method according to the paragraph previous to thepreceding paragraph wherein step e) further comprises:

r) hard decoding said selected packet sample estimate to create hardencoded bits and new packet information bit estimates for reinsertioninto said information bit estimates list;

s) hard modulating said hard encoded bits to create a transmitted symbolestimate;

t) constructing the time domain channel estimate from said packetreceived samples hypothesis and said transmitted symbol estimates;

u) constructing the packet transmit sample estimate from saidtransmitted symbol estimate;

v) convolving said time packet transmit sample estimate with said timedomain channel estimate to create the time domain channel appliedreceived samples estimate; and in parallel with steps t) and u);

w) constructing the frequency domain channel estimate from said packetreceived samples hypothesis and said transmitted symbol estimates;

x) multiplying said frequency domain channel estimate with saidtransmitted symbol estimates to create packet received symbol estimates;then

y) constructing the frequency domain channel applied received samplesestimate from the packet received symbol estimates; and

z) multiplexing the time domain channel applied received samplesestimate for reinsertion into said list.

With reference to FIGS. 10 to 18, the following blocks are used forreceiver signal processing techniques in accordance with the secondembodiment;

-   -   OFDM Soft Output Decode 288    -   OFDM Hard Output Decode 222    -   Encode 224    -   Soft Modulate 230    -   Hard Modulate 226    -   Acquisition 204    -   Matched Filter 202    -   Sum 208    -   Subtract 212    -   Convolve 236    -   Multiply 240    -   Time to Frequency Conversion (dependant on system standard) 234    -   Time Domain Channel Estimator 232    -   Frequency Domain Channel Estimator 238    -   Time, Frequency Domain Multiplex 220    -   Samples Estimate List (including associated Controller) 206    -   Information Bit Estimates List (including associated Controller)        213

Table 1 and Table 2 provide a key for the number signals and process ineach figure and the reference numbers in the text.

TABLE 1 Signals 1002 Received Samples 1004 Windowed Matched ReceivedSamples 1006 Empty Sample Estimates 108 Previous Packet Received SamplesEstimate 110 Packet Received Samples Hypotheses 112 New PacketInformation Bit Estimates 114 New Packet Received Samples Estimate 116Noise Hypothesis 118 Completed Packet Information Bit Estimates 119Packet Transmit Symbol Estimates 120 Time Domain Channel AppliedReceived Samples Estimate 122 Frequency Domain Channel Applied ReceivedSamples Estimate 126 Hard Encoded Information Bits 128 Soft EncodedInformation Bits 130 Time Domain Channel Estimate 132 Packet TransmitSamples Estimate 134 Frequency Domain Channel Estimate 136 PacketReceived Symbol Estimates

TABLE 2 Functional Blocks 202 Bandwidth Limiting Filter-MatchedFilter-p(t) 204 Acquisition 206 Samples Estimate List 208 Σ-Add 210 OFDMSoft/Hard Decode and Re-transmit 212 Σ(−vc)-Subtract 213 Information BitEstimates List 214 OFDM Soft/Hard Decode and Re-modulate 215 HybridRe-transmit 216 TDCA—Time Domain Channel Application 218 FDCA—FrequencyDomain Channel Application 220 MDX—Time, Frequency Domain Multiplex 222OFDM Hard Output Decode 224 Encode 226 Hard Modulate 228 OFDM SoftOutput Decode 230 Soft Modulate 232 Time Domain Channel Estimator 234F→T - 802.11a Frequency to Time Domain Conversion 236 Convolve - LinearConvolution 238 Frequency Domain Channel Estimator 240 Multiply

The second embodiment of the invention is adapted for a Packet basedOFDM WLAN system (eg. IEEE 802.11a, IEEE 802.11g). A typical receiverfor such a system performs processing tasks in accordance with FIG. 10.The input into the system is a complex, oversampled baseband receivedsignal 1002 for each attached antenna. The signal received on eachantenna is passed through a band limiting filter 202 which is thenfollowed by a packet detection and synchronisation (Acquisition)processing block 204. This Acquisition block uses one or more of thematched filter antenna signals 1004. Once a packet is acquired it isdecoded using either hard or soft decoding techniques and passed on to ahigher processing layer (eg. MAC). The typical receiver structure FIG.10 may be modified to an iterative structure that provides interferencecancelling at the sample level.

Iterative Receiver Structure & Function

The input to the receiver is the oversampled digital I/O basebandsamples from each antenna connected to the receiver called the ReceivedSamples 1002. The Received Samples 1002 are windowed over time andpassed through a filter 202 matched to the pulse shape in order toproduce windowed matched received samples 1004. This constitutes theNoise Hypothesis 116 for the first iteration (n=1). For all proceedingiterations (n>1), the Noise Hypothesis 116 is provided by the feedbackof the interference signal. This is depicted in FIG. 11 by the nconditioned switch SW_(n).

An iteration of the receiver is a single execution of each of thefollowing processes:

-   -   Attempt to acquire a new Packet in the Noise Hypothesis 116        using the Acquisition 204 process.    -   If a new packet is found, add empty entries 1006 to the Samples        Estimate List 206 and Information Bit Estimates List 213. Each        entry in the Samples Estimates List 206 has a corresponding        entry in the Information Bit Estimate List 213.    -   Determine, from the evolution of both Samples and Information        Bit estimates list, Completed Packets {y₁ . . . y_(m)}, in the        Information Bit Estimates List 206.    -   Release to higher layer (MAC) then Remove Completed Packets {y₁        . . . y_(m)} from the Information Bit Estimates List 213.    -   Remove Completed Packets {y₁ . . . y_(m)} from the Samples        Estimate List 206.    -   Select a Packet k in the Samples Estimate List 206 to Process.    -   Add 208 the Previous Packet Received Samples Estimate 208 of        selected packet k from the Samples Estimate List 206 to the        Noise Hypothesis 116 to produce the Packet Received Samples        Hypothesis 110.    -   Generate new Packet Received Samples Estimate 114 and new        information bit estimates 112 for the selected packet k from the        Packet Received Samples Hypothesis 110 using OFDM Soft/Hard        Decode and Re-transmit process 210.    -   Update the selected packets' k previous information bit        estimates in the Information Bit Estimates List 213 with the New        Information Bit Estimates 112.    -   Update the selected packets' k previous Samples Estimate in the        Samples Estimate List 206 with the New Packet Received Samples        Estimate 114.    -   Subtract 212 the New Packet Received Samples Estimate 114 from        the Packet Received Samples Hypothesis 110 to produce the Noise        Hypothesis 116.

Iterations are continually performed until all packets have beenreleased from the Information Bit Estimates List 213. Once this statehas been reached, the lists 206, 213 are cleared, the time window isupdated and the entire process repeated.

Iterative Interference Cancelling

Interference cancelling at the sample level requires the generation ofNew Packet Received Samples Estimate 114 for each antenna using the OFDMSoft/Hard Decode and Re-transmit 210 process for every Packet found bythe Acquisition 204 process. Each packet's New Packet Received SamplesEstimate 114 are stored in the Samples Estimate List 206. Theinterference cancelling structure requires that each packet Adds 208 itsPrevious Packet Received Samples Estimate 108 to the Noise Hypothesis116 before the Soft/Hard Decode and Re-transmit 210 process to producethe Packet Received Sample Hypothesis 110 for each antenna. The NewPacket Received Samples Estimate 114 produced by the Soft/Hard Decodeand Re-transmit 210 process are then Subtracted 212 from the PacketReceived Sample Hypothesis 110 to generate an updated Noise Hypothesis116. The New Packet Received Samples Estimate 114 are also used toupdate the Samples Estimate List 206. The Noise Hypothesis 116 is thenfed back through the system (minus the latest estimated contribution ofthe previously processed packet) providing Iterative InterferenceCancelling. FIG. 11 provides a graphical reference for this process.

Samples Estimate List

The Samples Estimate List 206 contains the New Packet Received SamplesEstimate 114 as generated by the OFDM Soft/Hard Decode and Re-transmitprocess 210 for each receive antenna for each Packet found by theAcquisition 204 process.

For each iteration, a packet to iterate (k) is selected from the SamplesEstimate List 204. The selection k can be based on numerous metricse.g., sorted signal power, the minimum number of processing cyclesperformed, order of arrival. This selection is depicted by the kcontrolled switch SW_(k) in FIG. 11, where k is the current selectedpacket.

Information Bit Estimates List

The Information Bit Estimates List 213 contains the latest New PacketInformation Bit Estimates 112 as generated by the OFDM Soft/Hard Decodeand Re-transmit 215 process for each Packet found by the Acquisition 204process.

Each iteration provides an opportunity to release Completed InformationBit Estimates 118 to higher layers (e.g. MAC). The choice of whichpackets are complete is made by evaluating a metric for each packet inthe Samples Estimate List 206. For example, this metric may be based onindicators such as signal power, the number of iterations performed andnumber of completed packets. These metrics are then compared to a targetvalue. All packets that meet their target are marked for release fromthe Information Bit Estimates List 213.

For each packet acquired there is an entry in both the Samples EstimateList 206 and the Information Bit Estimates List 213. The selection ofcompleted packets is depicted by the {y₁ . . . y_(m)} controlled switchSW_(y) in FIG. 11, where {y₁ . . . y_(m)} is the list of CompletedPacket Information Bit Estimates. A feature of the iterative receiverstructure is that the packet's Packet Received Samples Estimate 114remain subtracted from the Noise Hypothesis 116 even after it isreleased and its corresponding entries in both lists removed.

Hybrid Re-Transmission

The Hybrid Re-transmission 215 process is depicted in FIG. 12 and FIG.13. It uses both Time Domain Channel Application 216 and FrequencyDomain Channel Application 218 processes to generate a New PacketReceived Samples Estimate 114. Both processes use the Packet ReceivedSamples Hypothesis 110 for each antenna and Packet Transmit SymbolEstimates 119 to create Channel Applied Received Samples Estimate 120,122 for each receive antenna. The Time Domain Channel Application 216process produces a Time Domain Channel Applied Received Samples Estimate120. The Frequency Domain Channel Application 218 process produces aFrequency Domain Channel Applied Received Samples Estimate 122. TheChannel Applied Received Samples Estimate 120, 122 are then multiplexed220 together to form the New Packet Received Samples Estimate 113 foreach antenna. Each of these processes is described in further detailbelow.

Time Domain Channel Application (TDCA)

The Time Domain Channel Application 216 process is further expanded inFIG. 16. The Time Domain Channel Estimator 232 produces a Time DomainChannel Estimate 130 for each receive antenna using the Packet TransmitSymbol Estimates 119 from the OFDM Soft/Hard Decode and Re-modulate 214process (see FIG. 14 and FIG. 15) and the Packet Received SampleHypothesis 110 for each antenna. The Frequency to Time Conversion 234then produces a Packet Transmit Samples Estimate 132 using the PacketTransmit Symbol Estimates 119. The Packet Transmit Samples Estimate 132and Time Domain Channel Estimate 130 for each antenna are then linearlyconvolved via the Convolve 236 process to produce the Time DomainApplied Received Samples Estimates 120 for each antenna.

Frequency Domain Channel Application (FDCA)

The Frequency Domain Channel Application 218 process is further expandedin FIG. 17. The Frequency Domain Channel Estimator 238 produces aFrequency Domain Channel Estimate 134 for each antenna using the PacketTransmit Symbol Estimates 119 from the OFDM Soft/Hard Decode andRe-modulate 214 process and the Packet Received Sample Hypothesis 110for each antenna. The Packet Transmit Symbol Estimates 119 are thenmultiplied, one OFDM symbol at a time, by the Frequency Domain ChannelEstimate 134 via the Multiply 240 process to produce the Packet ReceivedSymbol Estimates 136. The Packet Received Symbol Estimates 136 are thenconverted into the Frequency Domain Channel Applied Received SamplesEstimate 122 using the Frequency-To-Time process 234.

Time, Frequency Domain Channel Application Multiplexing (MUX)

Referring now to FIG. 13, the Multiplexing 220 process takes the TimeDomain Channel Applied Received Samples Estimate 120 and the FrequencyDomain Channel Applied Received Samples Estimate 122 and multiplexesthem together to produce a hybrid New Packet Received Samples Estimate114.

OFDM modulation scheme such as those used in this second embodiment,commonly employ a cyclic prefix to combat multi-path interference. Also,due to time dispersion characteristics of both the radio channel andband limiting filters, there are tails at the beginning and end of theNew Packet Received Samples Estimate 114. New Packet Received SamplesEstimate 114 corresponding to the OFDM portion of the signal are takenfrom the Frequency Domain Channel Applied Received Samples Estimate 122.The remaining samples in the New Packet Received Samples Estimate 114are taken from the Time Domain Channel Applied Received Samples Estimate120. In this embodiment those samples comprise the cyclic prefix andtail portions of the New Packet Received Samples Estimate 114.

An example of multiplexer mapping is shown in FIG. 18.

Preferred Area of Application

The preferred areas of application for the second embodiment of thepresent invention are OFDM receivers that may be used with IEEE 802.11a,IEEE 802.11g, IEEE 802.16 and HiperLAN Wireless Local Area Network(WLAN) standards. However, the invention disclosed is useable in anypacked based OFDM communications system as would be understood by theperson skilled in the art.

With reference to FIGS. 19 to 23 a third embodiment is described whichstems from the realization that reducing the distortions in one or moreof the raw signals arriving at a receiver used to provide a decisionstatistic leads to an overall improvement in the decision statisticitself. Furthermore, appropriate selection of the means of reducingthese distortions leads to a more reliable determination of packetarrival time.

In one aspect the third embodiment provides a method and apparatus forcommunicating in a multiple access communication network bysynchronizing packets arriving at a receiver comprising:

receiving a packet input signal;

determining a correlation signal corresponding to the packet inputsignal;

processing the input and correlation signals such that at least one ofthe input signal and the correlation signal are filtered;

determining a decision statistic by combining a power component of theprocessed correlation signal with a power component of the processedinput signal;

nominate a point in time given by a predetermined threshold condition ofthe decision statistic as a received packet arrival time.

The processing of at least one of the input and correlation signals isperformed by one of a centre weighted filter having a triangular impulseresponse, a root raised cosine filter, a Hanning window filter, aHamming window filter, or a combined Hanning/Hamming window filter. Thepredetermined threshold condition may be one of the decision statisticcrossing the predetermined threshold or a maximum of the decisionstatistic occurring above the predetermined threshold. The determinationof the correlation signal may be performed every Kth sample of a sampledpacket input signal, where K is an integer greater than or equal to 1.The third embodiment of the present invention is described in moredetail below.

Power Averaging Mask for FFT Window Synchronisation

Synchronisation of packets transmitted, especially over wireless media,is ordinarily achieved by employing a preamble comprised of severalrepetitions of the same signal and correlating the received signal witha delayed version of itself. The delay may be chosen to equal theduration of the repeated signal component defining the preamble. Theoutput power of this correlation process is then usually normalisedagainst the average power in the received signal. The point at which thenormalised correlator output exceeds a threshold is selected as thepacket arrival time. This technique has a number of deficiencies. Forexample, it does not optimally exploit the statistics of the correlatoroutputs and thus may introduce larger error margins in the determinationof data packet timing. In this third embodiment, a method is disclosedwhich permits a more accurate determination of arrival time of a datapacket. Thus synchronisation errors may be reduced and, consequently,packet loss rates are reduced. Specifically, the method uses a linearfiltering approach to interpret the correlator outputs prior to powersbeing calculated, thereby improving the quality of the statistic usedfor packet synchronisation. This is achieved primarily due to the noisesuppression properties of the filter. The shape of the linear filter maybe optimally designed against the characteristics of the preamble andthe radio channel. An example would be a root raised cosine filter, or aHanning/Hamming window filter. One preferred embodiment of the inventionis the use of a centre weighted average filter with a triangular impulseresponse for application to the correlator outputs. This filter enablesmore accurate location of the time of the packet arrival than isachievable otherwise and has an efficient implementation. It is alsoproposed to use the maximum correlation power, once a threshold isexceeded, as the decision point rather than the time at which thecorrelation power first crosses a threshold. Those practiced in the artwill recognise that this method has potential application to anycommunication system that uses a repetitive preamble for packetsynchronisation. The inventor has recognised that filters are widelyused in general applications and that the synchronisation of packets maybe treated as a filtering problem. Accordingly, the inventor proposes touse raw correlator outputs as a preferred filter input. The use of acentre weighted (or other) filter on the correlator outputs prior topower calculation is used as a measure of the arrival timing of apacket. Threshold testing of the normalised power of the received signalcorrelated with a delayed version of itself is also contemplated. Thedelay is equal to the repetition size of the preamble. The normalisationis achieved by dividing by the sliding window averaged power of thereceived signal. In this third embodiment it is particularlyadvantageous to provide a receiver with the following functions:

Filtering of raw correlator outputs;

Centre weighted averaged filter, preferably a triangular filter whichhas an efficient implementation;

The above allows for basing a decision point on the maximum ofcorrelator output power rather than a first level crossing leading tobetter characterisation of packet timing to avoid packet transmissionloss/inefficiency. The third embodiment may comprise a receivertechnology for packet data transmissions where a repetitive preamble isdeployed to determine packet data timing and allowing for adaptivedesign of filter form against the statistics of the radio channel.

Field of Application

The third embodiment technology applies to a point to pointcommunications link where transmissions are made using a waveformstructure that has a preamble of a particular type. Specifically thepreamble may be formed by one or more repetitions of a base signal. Thefunctional device embodying the technology preferably resides in thebaseband receiver processor Rx of a general receiver 190, as previouslydiscussed and, in this embodiment, in the exemplary form of a wirelessmodem 190 as shown in FIG. 19. The relative logical location of thebaseband receiver Rx is shown in FIG. 19 as the “Baseband Rx”.

In more detail, in packet based communications systems the timing of thearrival of a packet is determined at the receiver 190. Once this timingis determined the alignment of the remaining (typically data bearing)portions of the packet may be determined using a-prior knowledge of thepacket structure. Therefore without accurate determination of the packettime packet errors may be prevalent. A common technique employed is totransmit a preamble at the start of the packet transmission that has aspecial structure permitting efficient arrival time determination at thereceiver 190. This structure requires the repetition of a short signalseveral times in the preamble. The structure of a typical packet isshown in FIG. 20 where the Sync Word (SW) is repeated several times atthe beginning of the transmission.

The conventional time synchronisation technique correlates the receivedsignal with a delayed version of itself. This delay may be set to thelength of the Sync Word and the correlation length may be set to thenumber of SW repetitions (L) minus one. This correlation is implementedevery sample (or every K^(th) sample where K is small, e.g. 4). If thereceived sample sequence is {r_(i−1), r_(i), r_(i+1), r_(i+2), . . . }then the correlator output at time i is

$\rho_{i} = {\sum\limits_{j = i}^{i + {N{({L - 1})}}}{r_{j}^{*}r_{j + N}}}$

This correlation value is compared with the power in the observedsequence

$\sigma_{i} = {\sum\limits_{j = i}^{i + {N{({L - 1})}}}{r_{j}^{*}r_{j}}}$

to form a decision statistic |ρ_(i) ²|/σ_(i) ². The arrival time i ischosen when this metric exceeds a threshold.

The inventor has identified that any noise present in the receivedsequence r_(i) is amplified by the squaring process and may cause thesynchronisation technique to pick the incorrect arrival time. Ratherthan waiting for the statistic to cross a threshold, the algorithm maybe adjusted to select the maximum statistic by including a small amountof decision delay. This maximum is chosen from those statistics abovethe threshold. A number of statistics crossing a given threshold isshown in FIG. 21.

Preferred Method

In this method according to the third embodiment of the invention theinventor exploits the profile of the autocorrelation of the preamble inorder to mitigate the negative effects of noise of the timesynchronisation performance. This may be achieved by filtering thesequences ρ_(i) and σ_(i) by a centre weighted low pass filter. Notethat this filter is applied prior to the subsequent squaring of thesequences for decision statistic generation. Any noise presence will bebetter suppressed by filtering prior to squaring. The filter may bedesigned against the autocorrelation properties of the preamble but in apreferred embodied a triangle filter is employed.

A triangle filter has an impulse response that is triangular in nature,specifically the coefficients (taps) of the (discrete time) filter are

$f_{i} = \frac{N - {i}}{N^{2}}$

as shown in FIG. 22. If the filter described above is applied to theunderlying sequences (ρ_(i) and σ_(i)) then a typical result would be asshown in FIG. 23. It can be seen that the threshold crossing techniquehas benefited from the application of the filter, since it is now closerto the maximum as seen by inspection of FIG. 23. The effect of the noisehas also been reduced therefore enhancing both the maximum and thresholdcrossing techniques. The preferred method is to apply the filter to bothraw sequences, compute the metric using the filtered sequence and to usethe maximum of the statistic that is above the threshold.Advantageously, a more accurate synchronisation of arrival time isachieved by filtering of the correlator output and power measurementprocessing prior to decision statistic generation; using a maximumsearch within a window defined by a threshold on the decision statistic.

By accurately estimating the arrival time of the preamble (and thereforethe packet), the number of packet decoding failures may be significantlyreduced. Apart from improving the chance of recovering the data payloadthis has flow on effects to the network users since both network controland data packets are now more reliably recovered.

With reference to FIGS. 24 to 31 a fourth embodiment of the presentinvention is described in which the solution offered stems from therealization that receiver sensitivity may be improved by improvingchannel estimates using symbol estimates from the encoded portion of apacket and iteratively updating these channel estimates based onrecently received data symbol channel estimates. A further aspect of thefourth embodiment resides in transforming each received data symbol tothe frequency domain to enable the release of time smoothed channelestimates for improved decoding.

Advantageously, in the fourth embodiment, each OFDM symbol may bedecoded more than once by obtaining a channel estimate for Symbol n,decoding symbol n, updating the channel estimate for symbol n, updatingthe channel estimate for symbol n−1 (by time domain smoothing from thenew channel estimate for symbol n), decoding symbol n−1, updatingchannel estimate n−1.

In accordance with a fourth embodiment the present invention provides amethod and system of tracking time varying channels in a packet basedcommunication system comprising:

a) initializing a channel estimate reference based on an initial channelestimate derived from a received packet preamble;

b) updating the channel estimate reference based on a packet data symbolchannel estimate in a coded portion of the current and all previousreceived data symbols;

c) repeating step b) at the arrival of subsequent packet data symbols.

The method preferably comprises storing the channel estimate referencein a channel estimate data base at the receiver. The method preferablycomprises transforming the packet data symbol channel estimates to thefrequency domain prior to updating the stored channel estimate referenceto provide a time smoothed channel estimate reference. The method alsopreferably comprises for each subsequent received data symbol withinstep b), pipelining the steps of demodulating, modulating, and updatingthe channel estimate reference with the further step of FEC decoding.

In the current state of the art, high mobility high bandwidthtransmission of information is limited by the inability of receiverprocessing techniques or methods to track the time varying nature of theradio channel and its effect on the transmitted signal and its waveform.Thus, related art systems for high mobility transmission support onlylow data rates. In this fourth embodiment, a receiver technique thatexploits OFDM signal structures is disclosed and the fact that theseOFDM signals are error control coded. Thus high mobility, high bandwidthdata transmission is permitted. Additionally, the technique alsobenefits fixed communication radio networks by improving receiversensitivity. Specifically, the fourth embodiment has been achieved bydeveloping an algorithm that permits the reliable decoding of OFDMmodulated packets of information that have been distorted by a rapidlyvarying radio channel, but without the need for compromising data rateby the excessive use of pilot or training signals.

In a preferred aspect of the fourth embodiment of the invention, analgorithm has been devised that may operate on a per OFDM symbol basisin order to avoid increased decoding latency and complexity.Correspondingly, in this embodiment, three statistics are exploited: thefrequency domain statistics of the radio channel at the OFDM symbolrate; time domain statistics of the radio channel across OFDM symbolsand; the outcomes of each decoded OFDM symbol. These statistics are usedto estimate the radio channel from OFDM symbol to OFDM symbol. When anew OFDM symbol arrives the channel and data estimates are updated forthe corresponding symbol and some small number of previous symbols. Inthis manner each OFDM symbol is decoded more than once with an improvedchannel estimate each time. Prediction of the radio channel from thereceived signal and knowledge of the preamble of the packet is deployedto initialise the process. That prediction uses the statistics of theradio channel. It will be evident to those practiced in the art thatthis embodiment permits the effective decoding of OFDM packets inrapidly varying radio environments. Thus it offers benefits in terms ofsupporting increased mobility at increased spectral efficiencies. Itachieves this without increasing the implementation complexity, orlatency, while simultaneously increasing receiver sensitivity. In thisregard, it has potential in both high mobility and in fixed wirelessnetworks. Those practiced in the art will recognise that this embodimentmay be applied to any wide band modulation technique that shares acommon underlying channel model similar to the preferred embodimentabove. Some examples are the addition of multiple receive antennas,multi-carrier OFDM or multi-carrier CDMA.

Advantageously, the fourth embodiment provides:

-   -   Iterative channel and data estimation whereby the initial        estimates are improved using data aided techniques.    -   Frequency domain smoothing stored across OFDM symbols enabling        release of time smoothed channel estimates for improved        decoding.    -   Decoder outcomes derive channel estimates stored in “CEDB”        (channel estimate data base) described in more detail, below.    -   Prediction of channel from CEDB to start up OFDM symbol loop        based processing.    -   Consequent low latency, high bandwidth high mobility data.

In this fourth embodiment a baseband digital receiver technology thatenables the effective reception of high data rate signals from a mobiledevice travelling at high speed is disclosed. A brief performanceanalysis is also presented.

Field of Application

This technology applies to a point to point communications link wheretransmissions are made using coded Orthogonal Frequency DivisionMultiplex (OFDM). In general, coded OFDM transmissions are formed by

1. forward error correction (FEC) encoding, over one (OFDM) symbolduration, the information bits, then

2. conventional OFDM modulation.

The FEC coding over one OFDM symbol may be block coded or the coding maycontinue across multiple OFDM symbols but per OFDM symbol decodingtechniques must be available. The receiver will exploit the coding onthe OFDM symbols to improve performance.

As with the third embodiment, the functional device embodying thetechnology preferably resides in the baseband receiver processor Rx of areceiver 190 in the exemplary form of a wireless modem 190 as shown inFIG. 19. The relative logical location of the baseband receiver Rx isshown in FIG. 19 as the “Baseband Rx”.

Latency and OFDM Symbol based Processing Loops

In packet based communications systems it is important to implement thereceiver processing with as little delay between the arrival of signalsand the decoding of the bits contained in the signal as possible. Thisis important since the turn-around time for acknowledgements is asignificant driver in the network performance. In OFDM modulated systemsthis requirement typically forces the use of per OFDM symbol processing.That is, when a new OFDM Symbols worth of signal arrives the Baseband Rxshould release an OFDM symbols worth of information bits. The delaybetween the information enabling the decoding of an OFDM Symbol and theoutcomes of decoding the Symbol must be of the order of a few OFDMSymbols duration.

OFDM Channel Estimation in Mobile Environments

In mobile radio communications systems coherent receiver designstypically require the use of accurate channel estimation methods in thebaseband receiver. The channel to be estimated is a multipath fadingchannel induced by motion and reflections in the field. Among otheruses, the channel estimate is employed to drive the FEC decoder, acritical aspect of the receiver. In the case of OFDM modulated signalsthe channel is normally measured in the frequency domain, after thereceived signal has been sliced up into OFDM Symbol sized pieces. Inmobile communications systems the channel over which the signal travelschanges with time and, if the vehicle speed is high enough, the channelmay change during the reception of a packet. In related art receivertechniques it is assumed that the multipath fading channel is invariantover the packet enabling the one-off estimation of the channel at thestart of the packet. In most standards (e.g. IEEE 802.11a) a preamble istransmitted at the start of a packet for exactly this purpose.

Preferred Method

In this method according to a fourth embodiment the partitioning of thereceived signal for OFDM to provide a convenient boundary for trackingtime varying channels is exploited. The channel estimate changes fromOFDM Symbol to OFDM Symbol. The preferred embodiment also exploits thefact that the OFDM symbol is encoded, enabling the use of decoded dataas training information for the channel estimator. The statistics of theway that the channel changes with time and frequency are also exploitedhere.

An estimate of the channel in the frequency domain is obtained. Theinventor defines the CEDB as a Channel Estimate Data Base containingchannel estimates for each OFDM symbol, smoothed in the frequencydimension (across sub-carriers), but not in the time dimension. Themethod comprises the following steps, as set out below, for a packetwith N OFDM symbols. Steps required for OFDM window synchronizationoccur prior to the processing shown here. The inner loop (3.4) is oflength, L, OFDM Symbols and enables iterative channel and dataestimation.

Ref Function 1 Estimate Time and Frequency Offsets based on Preamble 2Initialise CEDB based on Preamble 3 For Each OFDM Symbol (n=1:N) { 3.1 Transform Rx OFDM Symbol into Frequency Domain  (apply FFT) 3.2 Correct Rx OFDM Symbol for Time and Frequency offsets 3.3  GenerateChannel Estimate for OFDM Symbol n by prediction  from CEDB 3.4  ForEach recent OFDM Symbol (m=n:−1:n−L) { 3.4.1   Demodulate OFDM Symbol musing Channel Estimate 3.4.2   FEC Decode OFDM Symbol (outcomes alsoreleased to upper   layer) 3.4.3   Generate Training by remodulating FECDecoder Outcomes 3.4.4   Update CEDB using Training and Corrected RxOFDM   Symbol 3.4.5   Generate Channel Estimate for OFDM Symbol m−1 from  CEDB  } }The channel prediction (step 3.3 above) and generate channel estimate(step 3.4.5 above) both apply CEDB time domain smoothing across OFDMsymbols in their implementation. The strength of the smoothing (acrossSub-Carrier and OFDM Symbol dimensions) are independently controlled bya process not described here.Advantageously, the fourth embodiment provides:

1. Iterative Channel and Data Estimation whereby the initial estimates(resembling those that would be obtained conventionally) are improved(step 3.4) using data aided techniques.

2. Frequency Domain Smoothing stored across OFDM Symbols enablingrelease of time smoothed channel estimates for improved decoding (steps2, 3.4.4).

3. Decoder outcomes drive channel estimates stored in CEDB (steps 3.4.3,3.4.4).

4. Prediction of Channel from CEDB to start up loop based processing(step 3.3).

Parallelism may be exploited for implementation purposes by twoprocesses running in parallel comprising.

1. demodulation, modulation and channel estimation stages (steps 3.4.1,3.4.3, 3.4.4 & 3.4.5), and

2. FEC Decoding (step 3.4.2)

While Process 1 is working on OFDM Symbol n, Process 2 is working onOFDM symbol n−2. This offset requires the predictor in Ref 3.3 to lookahead one extra OFDM symbol.

The benefits obtained by use of this embodiment's technology are nowdescribed.

Complexity

By exploiting pipelining of the FEC decoder function the most difficultaspect of the receiver device is fully exploited while maintaining ahighly adaptive capability in terms of the propagation environment.

Sensitivity

By accurately estimating the channel, the performance of the decoderstage may be significantly improved (typically in excess of 1 dBincrease in receiver sensitivity). This has been found to be the caseeven for time-invariant channels and is realized by exploiting datasymbols for training purposes. In the case where mobility exists theability of the receiver to track the channel in time allows the receiverto operate effectively where conventional systems may fail. At the sametime, the benefits of iterative (multi-visit) estimation of the datasymbols are realized.

Latency

By employing per OFDM symbol processing and pipelining the FEC decoderthe inventor has obtained the earliest possible release of high qualitydata estimates. Therefore the receiver operates without increasinglatency relative to conventional techniques. It should be noted thatconventional techniques may fail in high speed mobile conditionsPerformance Analysis

In this section an example of the data and channel estimates that areobtained using conventional, idealised and the proposed receiverprocessing techniques are provided. The attributes of the communicationslink used in the example are shown in the table below.

Value Unit Quantity Bandwidth 16.0 MHz Carrier Freqency 5.0 GHz NumberSubCarriers 256 SubCarriers OFDM Symbol Duration 16 us OFDM Symbols PerPacket 38 OFDM Symbols Mobile Unit Velocity 30 ms⁻¹ CoherenceFrequency3.0 MHz Bits Per SubCarrier 2 Bits Pilot SubCarrier Spacing 32SubCarriers Eb/No 8.0 dB FEC Rate 1/2 FEC Memory 5 Derived ChannelCoherence 48.0 SubCarriers Frequency Channel Coherence Time 62.5 OFDMSymbols Packet Length 640.0 us Doppler Frequency 0.5 kHz

The actual radio channel (measured after FFT application in thereceiver) is shown in FIG. 24. The rapid phase rotations in the Phaseplot result from FFT window misalignment and residual intermediatefrequency in the down-conversion step. These are both real-worldimpairments. The receiver estimates both of these parameters and may becompensated for them on a symbol by symbol basis. The result of thiscorrection is shown in FIG. 25. Note that this figure represents theactual radio channel corrected by an estimated quantity and is shownhere for assessment purposes. An objective of the receiver is toaccurately estimate this corrected channel.

Conventional Processing

In conventional processing the radio channel is estimated based on thepreamble only. The main restriction with this approach is that the radiochannel (after correction) must be invariant across the frame. As shownin FIG. 25 this is not the case since there is a phase change at aroundOFDM symbol 30 in some of the sub-carriers. It is therefore expectedthat decoder failures starting at around OFDM Symbol 30 of the packetwill occur. This is indeed the case as shown in FIG. 26.

Preferred Method (Perfect Training Symbols)

FIG. 28 shows the performance of the proposed system is shown with thepossibility of decoder failures for training symbol generationeliminated. The decoder outcomes for data recovery are still recordedhence the errors in FIG. 28. This represents the best possible case fordata aided radio channel estimation. It is possible to compare thisresult with that obtained using decoder outcomes for training in thefollowing section. Note that the number of errors has dramaticallyreduced relative to the conventional technique.

Preferred Method

In this section the performance of the proposed method is evaluated. TheCEDB is shown in FIG. 29 and represents a good estimate of the radiochannel even though smoothing across OFDM symbols has not been employed.The smoothing across sub-carriers is however evident. Once the smoothingacross OFDM symbols is employed a very good match to the actual radiochannel is observed, as shown in FIG. 28. As can be seen in FIG. 28 andFIG. 29 the error obtained using the proposed method results in the sameerror pattern as the idealised method. The error performance is vastlysuperior to the conventional method as shown in FIG. 26.

With reference to FIGS. 30 to 34 a fifth embodiment is described, whichstems from the realization that receiver sensitivity may be improved byuse of the outputs of a receiver's decoder as additional pilot ortraining symbols and updating these iteratively with each symbolreceived for the recalculation of a channel estimate, and frequency andtime offsets as they vary throughout a packet.

In one aspect the fifth embodiment provides a system and method ofcommunicating in a multiple access packet based network by estimatingtime varying channel impairments, where channel impairments comprisechannel variation, signal frequency offset and signal time offset,comprising:

a) initializing a set of channel impairment estimates based on initialpilot and preamble symbols included in a received packet;

b) performing a decoder operation which comprises processing the set ofchannel impairment estimates and the received packet to determine a setof transmit symbol estimates;

c) updating the set of channel impairment estimates through use of thedetermined set of symbol estimates and received packet;

d) repeating steps b) and c).

In another aspect the fifth embodiment provides a system and method ofcommunicating in a multiple access network by time varying channelestimation in a receiver for receiving transmitted packets, comprising:

a) estimating a frequency offset based on information included in areceived packet preamble;

b) correcting a received signal using the estimated frequency offset;

c) determining a channel estimate using information included in thereceived packet preamble;

d) transforming a sample sequence of the received signal into thefrequency domain such that the sample sequence includes OFDM symbols andintervening cyclic prefixes;

e) performing a decoding operation which comprises processing thedetermined channel estimate and received packet;

f) generating a transmission sample sequence using the decoding resultsand information in the received packet preamble;

g) transforming the transmission sample sequence into the frequencydomain;

h) updating the determined channel estimate by combining the receivedsample sequence and the transmission sample sequence in the frequencydomain;

i) repeating steps e) to h).

In a further aspect the fifth embodiment provides a system and method ofcommunicating in a multiple access network by time varying channelestimation in a receiver for receiving transmitted packets, where thereceiver retrieves OFDM symbols from a received signal and transformsthe retrieved symbols to the frequency domain, comprising:

a) determine a matrix of training symbols comprised of symbol estimatesderived from a decoder;

b) determine a matrix of frequency domain received OFDM symbols;

c) determine an intermediate channel estimate matrix by multiplying theOFDM symbol matrix by the conjugate of the training symbol matrix;

d) determine an intermediate matrix of training weights comprising theabsolute value of the training symbol matrix;

e) perform a smoothing operation on both intermediate matricescomprising 2 dimensional filtering;

f) determine the channel estimate by dividing the smoothed channelestimate matrix with the smoothed training weight matrix.

In yet another aspect the fifth embodiment provides a system and methodof communicating in a multiple access network by estimating offsets in areceiver for receiving transmitted packets, comprising:

a) determine a matrix of received OFDM symbols;

b) determine a matrix of conjugated data symbols wherein the datasymbols comprise one or more of preamble, training and estimatedsymbols;

c) determine a 2 dimensional Fourier transform matrix comprised of thereceived symbol matrix multiplied with the conjugated symbol matrix;

d) filter the Fourier transform matrix;

e) determine time and frequency offsets by locating peak poweroccurrences within the filtered Fourier transform.

The fifth embodiment provides reliable estimation of channelimpairments. In the related art, that is, in the theoretical rather thanpractical context, decoder outcomes are employed to assist with theestimation of channel coefficients and synchronisation of receivedsignals in radio communications systems and radio networks. Thedifficulties encountered with these present theoretical approaches todecoder outcomes include the appropriate treatment of the uncertainty ofthese decoder outcomes in what would otherwise be conventional channelestimation and synchronisation techniques. In other words, thedifficulty of applying one-shot or preamble-only channel estimationtechniques or processing to an iterative process leads to less efficientand less accurate channel estimate and synchronisation performance. Withthis in mind, in this embodiment the use of a channel estimation and asynchronisation technique that employ an entire packet's worth ofdecoder outcomes (in addition to the preamble) is described. Whileothers also have advocated this approach (at least in general terms), inthe present embodiment, the specific method to manage uncertainty in thedecoder outcomes and subsequent processing are distinguished from therelated art by the features described here below. In this embodiment, inestimating the channel, the inventor first employs the frequency domainversion of the remodulated decoder outcomes and preamble as trainingsymbols. Then compute the frequency domain channel estimate from thistraining symbol sequence and from the frequency domain version of thisthe received signal. This may be achieved by either division or byminimum mean square error estimation or, via other estimationtechniques. Any errors in the decoder outcomes will be dispersed similarto the use of an interleaver and not have direct impact on a localregion of the channel estimate.

It should be noted that the channel estimation approach of the fifthembodiment is able to track the channel as it varies across the packetby slicing the packet up into segments that are assumed invariant. Thusthe practical impact of this embodiment is that more reliable channelestimates provide the opportunity for significantly improved informationpacket recovery in radio communications.

In another aspect, the synchronisation technique, the inventor employsthe preamble and decoder outcomes to remove the effects of datamodulation on the received signal and then applies a 2 dimensional FastFourier Transform. By then executing a peak power search estimates ofboth the residual time and frequency offsets are obtained. These maythen be employed to enable effective synchronisation.

In another aspect a channel estimator has been provided. This aspectemploys the outcomes of soft FEC Decoding (e.g. SOVA) to improve thequality of the radio channel estimate so that repeating the decodingstep, using the new channel estimate, offers improved outcomes. Thesesoft outputs are used to generate soft training symbols. Firstly,multiply the received OFDM Symbol matrix by the conjugate of the SoftTraining symbols to get an intermediate raw channel estimate. Thencompute a further intermediate matrix of training weights equal to theabsolute value, or absolute value squared, of the each of the softtraining symbols. Both of these matrices are then smoothed using filtersbased on channel statistics. The channel estimate is then obtained bydividing the smoothed raw channel estimate by the smoothed trainingweight matrix in an element wise fashion. The impact of this aspect onhigh mobility, high data rate communications networks will be evident tothose practiced in the art. Accordingly, lower packet loss rates impacton network capacity. The method also increases the ability toaccommodate rapidly changing radio channels and more reliably decodedata transmissions. Likewise, increased receiver sensitivity leads toreduced packet loss rates and increased range for OFDM based systemswith high velocity nodes.

The following acronyms are used in this description of the fifthembodiment.

APP A-Posterior Probability DSP Digital Signal Processor FEC ForwardError Correction FFT Fast Fourier Transform IF Intermediate FrequencyIFFT Inverse FFT OFDM Orthogonal Frequency Division Multiplex RF RadioFrequency SOVA Soft Output Viterbi Algorithm

This fifth embodiment of the invention provides a suite of basebanddigital receiver technologies that enables the effective reception ofhigh data rate signals from a mobile device travelling at high speed.

Field of Application

This suite of technologies applies to point to point communicationslinks where transmissions are made using coded Orthogonal FrequencyDivision Multiplex (OFDM). As noted above, coded OFDM transmissions areformed by

-   -   forward error correction (FEC) encoding, over one (OFDM) symbol        duration, the information bits, then    -   conventional OFDM modulation.

The FEC coding over one OFDM symbol may be block coded or the coding maycontinue across multiple OFDM symbols but per OFDM symbol decodingtechniques should be available. The receiver may exploit the coding onthe OFDM symbols to improve performance.

Typically the technology resides in the baseband receiver processor of awireless modem. This location is shown in FIG. 19 as the “Baseband Rx”

In packet based communications systems it is important to implement thereceiver processing with as little delay between the arrival of signalsand the decoding of the bits contained in the signal as possible. Thisis important since the turn-around time for acknowledgements is asignificant driver in the network performance. In OFDM modulated systemsthis requirement typically forces the use of per OFDM symbol processing.However as signal processing capabilities improve it is envisaged thatanother, more powerful option, will become available to systemdesigners. The more powerful technique will employ the entireobservation in making decisions about every bit transmitted (e.g. TurboCodes). In current techniques only a portion of the received signal isemployed to assist with the decoding of any particular information bit.Typically, a local channel estimate may be formed using a portion of theobservation and then decoding for that portion may be executed. Thebenefit of employing the observations, to follow, to assist with channel(or any other unknown parameter) estimation is currently not realiseddue to implementation complexity and performance of currently availableDSP technology. Here the fifth embodiment provides techniques thatemploy the entire observation to improve the channel estimation andhence reduce decoder errors. In addition, the transmitted waveform isoften structured to permit per OFDM symbol processing at the receiver.If this requirement is relaxed, frame based channel coding techniquesmay be applied to further improve the performance of the communicationslink. Examples of these techniques are the use of packet levelinterleaving and Block (e.g. Turbo) coding which may offer largeperformance benefits.

OFDM Channel Estimation in Mobile Environments

In mobile radio communications systems coherent receiver designs requirethe use of accurate channel estimation techniques in the basebandreceiver. The channel to be estimated is a multipath fading channelinduced by relative motion and multiple propagation paths between thetransmitter and receiver and residual errors due to Transmit/Receiveradio mismatch. The channel estimate is employed, among other uses, todrive the FEC decoder, a critical aspect of the receiver. In the case ofOFDM modulated signals the channel is normally measured in the frequencydomain, after the received signal has been separated into OFDM Symbolsized pieces and transformed via the application of an IFFT. In mobilecommunications systems the channel over which the signal travels changeswith time and, if the vehicle speed is high enough, the channel maychange over the duration of a packet. This translates to the channelexperienced at the start of the packet being substantially differentthat experienced at the end of the packet when viewed from the receiver.Related art receiver techniques assume that the multipath fading channelis invariant over the packet, enabling the calculation of a singlechannel estimate at the start of the packet to decode the entire packet.In most standards that use OFDM transmission schemes (e.g. IEEE 802.11a)a preamble is transmitted at the start of each OFDM symbol in order topermit estimation of the radio channel at the start of the packet.

However, the quality of the communications link may be increased byemploying the use of data aided techniques in the estimation of theradio channel. In this case, the result of applying the FEC decoder onthe received signal generates an estimate of the transmitted symbolswhich, while not absolutely accurate, are suitable for exploitation asadditional pilot symbols. Typical examples of data aided channelestimation for OFDM are implemented in the frequency domain andtherefore suffer power losses due to discarding of the cyclic prefixfrom each received OFDM symbol. The discarded cyclic prefix istheoretically useful for channel estimation and typically accounts for10-50 percent of the received signal energy. Since the transmittedsymbols determining the cycling prefix may be estimated at the receiver,this energy is potentially useful, as illustrated below, in theestimation of the radio channel and should not be discarded.

Frequency and Time Offset Estimation

Frequency offset arises due to the imprecise down conversion of thereceived signal from RF or IF to baseband. Time Offsets are commonlycaused by inaccuracies in the packet arrival time estimation due to theimpact of multipath fading channel and noise. Multipath, or Timedispersive, channels result in multiple copies of the transmitted packetarriving at the receiver at different times therein decreasing thecertainty in the time of arrival of the packet. Conventionally,estimates of the frequency and time offsets are initially made using thepreamble of the packet and maintained using pilot symbols, inserted bythe transmitter, throughout the packet (e.g. 802.11a). An example ofthis packet format for 802.11a is shown in FIG. 30.

Frequency offsets manifest as inter carrier interference and a constantphase rotation across OFDM Symbols and Time offsets manifest as phaserotations across the OFDM Sub-Carriers. The inventor assumes that fineInter-frequency offset estimation is required consistent with theresidual errors after an initial frequency offset correction. The phaseoffsets induced in the received symbols are due to the combined effectsof the data modulation, transmission across the radio channel,imprecision in the frequency synchronisation during down conversion andimprecise time of alignment of the OFDM symbols during the time tofrequency conversions. In order to estimate the radio channel, theeffect of the data symbols (be it preamble, pilot or unknown) on thereceived signal must first be removed, thereby leaving only the effectof the radio channel and time/frequency offsets. In the case ofpreambles and pilots the symbols are known a-priori and hence theirremoval is possible at the receiver. Using related art methods, theparts of the observation that are effected by data are not available toaide in the estimation of the frequency and time offsets since the datasymbols are not known at the receiver. The fifth embodiment, however,employs data aided techniques to significantly improve the performanceof the estimation by making many more symbols available to theestimation process.

Proposed Method

The method proposed here is an iterative process that uses the outputsof the decoder as additional pilot symbols for recalculation of thechannel estimate and for the recalculation of the frequency and timeoffsets as they vary across the packet. Collectively herein we shallrefer to effects of the multipath channel combined with the frequencyoffsets induced by the RF or IF to baseband conversion and the timeoffsets caused by time misalignments in the time to frequency conversionas channel impairments. On the first iteration, the channel impairmentsare estimated using the pilot and preamble symbols nominated by thetransmission scheme. These estimates are used to drive the initialexecution of the decoder and generate the first transmit symbolestimates. Iterations thereafter use the transmit symbol estimates ofthe previous iteration as new pilot symbols to aid in the estimation ofthe channel impairments. The new channel impairment estimates are thenused to re-run the decoder and generate new symbol estimates. Thisprocess may be repeated I times where I is the number of iterations andis an integer greater than equal to zero.

The details of the specific channel impairment estimators will bedescribed in the following sections.

Channel Estimation

Two methods are available for estimation of the radio channel. One maybe used when the radio channel is said to be invariant over the durationof the packet or discrete subsection thereof. The other is applicablewhen the radio channel varies over the duration of the packet.

Sequence Based Channel Estimation for OFDM

The sequence based channel estimator described here applies when thechannel is invariant over a packet or, any substantial fraction thereof.This technique exploits all of the available received energy and isimplemented prior to the OFDM symbol slicing conventionally employed inreceivers for OFDM signals. The steps executed are as follows

Ref Function 1 Estimate Frequency Offset using Preamble 2 CorrectReceived Signal for Frequency Offset 3 Estimate Channel using Preamble 4Convert Rx Sample Sequence to Frequency Domain 5 For Some Number ofIterations { 5.1  Decode Packet using Current Channel Estimate 5.2 Generate Tx Sample Sequence using Decoder Outcomes &  Preamble/Pilots5.3  Convert Tx Sample Sequence to Frequency Domain 5.4  EstimateChannel By Dividing Rx Sample and Tx Samples in  Freq Domain }

Steps 1 through 3 are common operations performed in typical OFDMreceivers. Step 4 would not normally be found in an OFDM receiver.Conventionally the received sequence is sliced up into small OFDM Symbolperiods, separated by Cyclic Prefix regions which are discarded. Each ofthese OFDM Symbols is transformed into the frequency domain by an FFTfor processing (channel estimation, decoding, etc) as in step 5.1. Step4 converts all parts of the received sample sequence that represents anentire packet or, selected portion thereof, including the cyclic prefixregions into the frequency domain to enable frequency domain channelestimate at the sequence level.

This requires the other steps (5.2 and 5.3) which produces a hypothesisof the entire packet's frequency domain transmitted signal. In thefrequency domain the received signal is equal to the transmitted signalmultiplied by the channel plus any noise. This fact is exploited in step5.4. The step in 5.4 could be replaced with an optimal linear estimatorbased on the Minimum Mean Squared Error criterion.

Channel Estimation with Soft Training Symbols

The channel estimator described here operates in the frequency domain ofa conventional OFDM receiver. It is assumed that the received signal hasbe sliced up into OFDM Symbols, the Cyclic prefix discarded and theresulting

OFDM Symbols converted to the frequency domain, via the use of an FFT.These processes are found in conventional OFDM receivers. The proposedmethod of the fifth embodiment is an iterative process that uses thesymbol estimate outputs of the FEC decoder as additional pilot symbolsor “Soft Training Symbols” in a re-estimation of the radio channel. Bydoing so (while noting these symbol estimate outputs may not be precise)the estimate of the radio channel is improved such that a subsequentexecution FEC decoder produces an improved result over the previousexecution.

Many different types of “soft output” decoders are available presently,including Soft-Output Viterbi Algorithms (SOVA), A-PosterioriProbability (APP)

Decoders and various types of Turbo Codes. These soft outputs are usedto generate soft training symbols according to techniques that may befound in the relate art literature, which would be understood by theperson skilled in the art. It is the use of these soft training symbolswhich requires careful consideration and an improved technique isproposed here.

In the absence of noise, and other impairments, a received OFDM Symbolis equal to the multiplication of the transmitted OFDM Symbol and thefrequency domain channel. If an OFDM system has N sub-carriers(frequency bins) then we may define vectors of length N to represent thetransmitted data d_(i) and radio channel h, for some OFDM Symbol periodi. The received OFDM symbol in this case is r_(i)=d_(i).*h_(i), wherethe operator ‘.*’ corresponds to element-wise multiplication of thevectors. In the case where d_(i) is known perfectly at the receiver(e.g. if it were a pilot symbol) then the channel could be recoveredperfectly in this ideal noise free case as

ĥ _(i) =r _(i) ./d _(i) =h _(i)

where, similar to the ‘.*’ operator, the ‘./’ operator corresponds to anelement-wise division of the vector elements. In data aided techniquesthe decoder outcome, {circumflex over (d)}_(i) is used instead of theactual transmitted data. This estimate is subject to errors. The fifthembodiment involves a technique that accounts for this uncertainty inthe “training” symbols. The method may be employed for time varying orinvariant radio channels and takes a slightly different form dependingof the channel variation. The following is a description of theestimator for time varying radio channels.

Assume the following is provided:

1. an entire packets worth of received OFDM Symbols R, and

2. an entire packets worth of soft training symbols D (some may be“hard” pilot symbols).

It is possible to structure these two objects as matrices as shown inFIG. 31 for M sub-carriers and N OFDM Symbols, where the rows aresub-carriers (tones or frequency bins) and the columns are OFDM Symbols(time). Firstly, multiply the received OFDM Symbol matrix by theconjugate (denoted X*) of the Soft Training symbols to get anintermediate raw channel estimate V=R.*D*. Note that the conventionalstep (as described above) would prescribe a division, not amultiplication. Then compute a further intermediate matrix of trainingweights T=|D| or other functions such as absolution value squared. Thenapply smoothing to both of these matrices using a two dimensional filter(f) matched to the channel coherence time and frequency. This filteroutcome may be approximated by implementing smoothing independently inthe time and frequency domains (rows then columns or vice versa) to savecomplexity. The estimate of the time varying channel is then derived as

Ĥ _(i) =f(V)./f(T)=f(R.*D*)./f(|D|)

The uncertainty in the decoder outcomes is accounted for in the stepwhere the absolute value of the training symbols was obtained. Smalltraining symbols result from uncertain soft output from the FEC decoderstep. A soft output FEC decoder will output a zero when a reliableestimate cannot be determined. Multiplication (in the R.*D* step) by azero effectively excludes that symbol estimate from the channelestimation process. Note that in the next iteration the symbol estimatemay have firmed up, due to improved statistics driving the FEC decoder,increasing its reliability and therefore it may now be included in thechannel estimation process. In the ideal case the decoder will outputcorrect, hard decisions and all data symbols will be used as perfecttraining to yield a very accurate channel estimate.

In the case that the channel is assumed time invariant across the packetthe filtering function simply adds up the column and resulting in acolumn that is assume to apply over the entire packet.

In some cases, an approach whereby the two dimensional filter f appliedto the raw channel estimate and training weight is different may bewarranted. In these cases the time varying channel estimate would be

Ĥ _(i) =f ₁(V)./f ₂(T)=f ₁(R.*D*)./f ₂(|D|)

where f₁ and f₂ implement different filters.

Joint Time and Frequency Offset Estimation using 2D FFT

In this aspect of the fifth embodiment we remove the effect of the dataon the phase difference between adjacent symbols in the OFDM receivedmatrix as shown in FIG. 31 and then apply a 2 Dimensional FFT. Thisremoval may be achieved by multiplying the observed OFDM Symbol matrixwith a corresponding matrix of conjugated data symbols be they preamble,training or estimated. The FFT output is then filtered to suppressnoise, and a search for the peak power across the resulting 2Dimensional space of metrics is executed. The filtering will have animpact on the maximum offsets that may be measured and it is thereforerecommended that only very weak filtering be employed. The location ofthe peak, in terms of relative position in the rectangle of FIG. 31,determines the time and frequency offsets.

The granularity and range of the estimation is limited as follows. Ifthere are M Sub Carriers and N OFDM Symbols then the range andresolution available from this technique is as shown in the following

Resolution Limit Frequency OFDM Symbol OFDM Symbol Offset Frequency/NFrequency Time Offset OFDM Symbol OFDM Symbol Duration/M Duration

An example for the system parameterised by is now given.

Parameter Value Number Of Tones 256 Number Of Symbols 20 Coherence Tones40 Coherence Symbols 50 Actual Freq Offset 0.05 Actual Time Offset 0.20

With the actual channel amplitude and phase shown in FIG. 32 and FIG. 33we get the metric shown in FIG. 34 for peak detection. Note that thepeak is in the expected relative position, i.e. a fraction of 0.05 alongthe OFDM Symbol dimension and a fraction of 0.2 along the sub-carrierdimension. These estimates match the actual time and frequency offsetsas shown in the above table of parameter values in the model.

By accurately estimating the channel, the performance of the FEC decoderstage is significantly improved, typically in excess of 1 dB increase inreceiver sensitivity. This is true even for time-invariant channels andis realized by exploiting data symbols for training purposes. In thecase where mobility exists the ability of the receiver to track thechannel in time allows the receiver to operate effectively where relatedart systems may fail. At the same time, the benefits of iterativeestimation of the data symbols are realized.

In a sixth embodiment the present invention provides a solutionpredicated on the use of firstly correlating the received signal at eachantenna of a multiple access communication network with a known signalpreamble and then statistically combining the correlated signal sequenceof each antenna based on estimated antenna signal strength. It should benoted that in order to determine the coefficients for combining aninitial timing estimate must be determined. The calculation of thesecoefficients will require, in practice, initial coarse timing andfrequency offset estimation by other means. The quality of the initialtiming estimate may be worse than that desired ultimately. The inventorconsiders further processing on the combined signal will lead to atiming estimate of high quality.

In a first aspect the sixth embodiment provides a system and method ofcommunicating in a multiple access packet network by synchronizing areceived signal in a multi antenna receiver comprising:

correlating a received signal observation at each of a plurality ofantennae with a known signal preamble to provide a received signalsequence;

determine a power signal of each received signal sequence;

combine the determined power signals in accordance with a time averagedweighting based on estimated antenna signal strength for each antenna;

determine a time of arrival for the received signal in accordance with apredetermined threshold condition.

An preferred aspect of the sixth embodiment of the invention comprises:

determining an estimate of the relative phase and amplitude coefficientsof a receiving channel for each antenna;

combining a received signal with the estimated coefficients to provide acomposite signal;

determining a time of arrival of the received signal by correlating thecomposite signal with a delayed version of itself.

In related art, metrics used for synchronisation are based on outputs ofcorrelators for the preamble of a packet. In the case of multiplereceive antennae, a method for either combining or deriving a new methodof metric generation for synchronisation is desirable. Related artschemes propose making decisions per antenna and then majority voting oradding the metrics prior to decision. Neither of these approachesaddresses sufficiently the variation of the signal statistics acrossantennae. The net result of this is degraded synchronisation accuracyand increased packet loss rates. A further issue relates to theeffective use of multiple antennae for data carriage but poor use ofmultiple antennae for synchronisation. In this case packets that couldotherwise be decoded may be missed by the synchronisation module.

In this sixth embodiment, we disclose a method for determining perantenna metrics and for subsequent combining across antennae in order togenerate a metric for time of arrival estimation. The method involvesessentially two steps. The per antenna metrics are derived bycorrelating the received signal with a known preamble in a first step.The power of the sequences for each antenna is determined and addedacross antenna according to the time averaged weight based on estimatedantenna signal strength. A threshold is then applied in order todetermine the time of arrival.

A further aspect of the sixth embodiment relates to obtaining a rapidestimate of the relative phase and amplitude of the channel on eachantenna and then to combine the received signal according to theconjugate of these coefficients. The processing would then proceed as inthe related art with correlation of this composite signal with a delayversion of itself. Application of this aspect of the sixth embodiment isin the synchronisation of wireless communication links involving thesimultaneous use of multiple receive antennae where the multipleantennae are used to increase the robustness of the communications linkprimarily through increased diversity.

In a further aspect, the signals from each antenna are combinedaccording to Minimum Mean Square Error criteria where the combiningcoefficients are dependent on a background noise measure on each antennaas well as the received signal energy. The processing would then proceedas in the related art with correlation of this composite signal with adelay version of itself.

It is particularly advantageous that the sixth embodiment provides for:a combining method for the metrics over antennae; currently does notrequire OFDM specific characteristics, and; a version with OFDMspecificity may be defined for clarity.

It will be appreciated by those skilled in the art, that the inventionis not restricted in its use to this particular application described,neither is the present invention restricted to its preferred embodimentwith regards to the particular elements and/or features described ordepicted herein. It will be appreciated that various modifications canbe made without departing from the principles of the invention.Therefore, the invention should be understood to include all suchmodifications within its scope.

While this invention has been described in connection with specificembodiments thereof, it will be understood that it is capable of furthermodification(s). This application is intended to cover any variationsuses or adaptations of the invention following in general, theprinciples of the invention and comprising such departures from thepresent disclosure as come within known or customary practice within theart to which the invention pertains and as may be applied to theessential features hereinbefore set forth.

As the present invention may be embodied in several forms withoutdeparting from the spirit of the essential characteristics of theinvention, it should be understood that the above described embodimentsare not to limit the present invention unless otherwise specified, butrather should be construed broadly within the spirit and scope of theinvention as defined in the appended claims. Various modifications andequivalent arrangements are intended to be included within the spiritand scope of the invention and appended claims. Therefore, the specificembodiments are to be understood to be illustrative of the many ways inwhich the principles of the present invention may be practiced. In thefollowing claims, means-plus-function clauses are intended to coverstructures as performing the defined function and not only structuralequivalents, but also equivalent structures. For example, although anail and a screw may not be structural equivalents in that a nailemploys a cylindrical surface to secure wooden parts together, whereas ascrew employs a helical surface to secure wooden parts together, in theenvironment of fastening wooden parts, a nail and a screw are equivalentstructures.

“Comprises/comprising” when used in this specification is taken tospecify the presence of stated features, integers, steps or componentsbut does not preclude the presence or addition of one or more otherfeatures, integers, steps, components or groups thereof.”

REFERENCES

-   [1] M. C. Reed, C. B. Schlegel, P. D. Alexander, and J. Asenstorfer,    “Iterative multiuserdetection for CDMA with FEC: Near-single-user    performance,” IEEE Trans. Commun., pp. 1693-1699, December 1998.-   [2] S. Marinkovic, B. S. Vucetic, and J. Evans, “Improved iterative    Parallel interference cancellation for coded CDMA systems,” in the    Proc. IEEE Int. Symp. Info. Theory, (Washington D. C.), p. 34, July    2001.-   [3] D. E. Catlin, Estimation, Control, and the Discrete Kalman    Filter, Springer Verlag, 1989.-   [4] P. D. Alexander, A. J. Grant, and M. C. Reed, “Iterative    detection on code-division multiple-access with error control    coding,” European Transactions on Telecommunications, vol. 9, pp.    419-426, September-October 1998.

1-38. (canceled)
 39. A method of communicating by tracking a plurality of time varying channels in a multiple access packet based communication network, each packet comprising a preamble portion including at least one data symbol, and a data portion including a plurality of data symbols, the method comprising: initializing a channel estimate reference from an initial channel estimate based upon the at least one data symbol in a preamble portion of a received packet; updating the channel estimate reference based upon a channel estimate of a current data symbol and at least one previously received data symbol from the plurality of data symbols in a data portion of the received packet; and repeating the updating upon receipt of at least one subsequent data symbol from the plurality of data symbols in the data portion of the received packet.
 40. The method according to claim 39 further comprising storing the channel estimate reference in a channel estimate database at a receiver device.
 41. The method according to claim 40 further comprising: for each data symbol, demodulating the data symbol based upon the channel estimate; decoding the data symbol; generating training data by remodulating the decoded data symbol; updating the channel estimate database based upon the training data; and generating the channel estimate for the data symbol based upon the updated channel estimate database.
 42. The method according to claim 39 further comprising transforming the channel estimate to a frequency domain prior to updating the channel estimate reference to provide a time smoothed channel estimate reference.
 43. The method according to claim 39 further comprising: for each subsequent data symbol, during the updating, pipelining demodulating and modulating; and updating the channel estimate reference by performing a forward error correction (FEC) decoding.
 44. The method according to claim 39 wherein the multiple access packet based communication network comprises an orthogonal frequency-division multiplexing (OFDM) wireless network.
 45. The method according to claim 44 wherein the updating of the channel estimate reference comprises calculations based upon at least one of a frequency domain statistic of a radio channel at an OFDM symbol rate, a time domain statistic of the radio channel across OFDM symbols, and outcomes of each decoded OFDM symbol.
 46. The method according to claim 39 wherein the updating is performed on a data symbol-by-data symbol basis.
 47. The method according to claim 39 wherein each previously received data symbol is decoded iteratively with each subsequent data symbol.
 48. The method according to claim 39 wherein the updating based upon the current and the at least one previously received data symbols is performed in parallel.
 49. The method according to claim 39 wherein the updating of the channel estimate reference is based upon the channel estimate of the current and all previously received data symbols.
 50. A wireless communications device for communicating by tracking a plurality of time varying channels in a multiple access packet based communication network, each packet comprising a preamble portion including at least one data symbol, and a data portion including a plurality of data symbols, the wireless communications device comprising: a controller; and a wireless transceiver cooperating with said controller for initializing a channel estimate reference from an initial channel estimate based upon the at least one data symbol in a preamble portion of a received packet, updating the channel estimate reference based upon a channel estimate of a current data symbol and at least one previously received data symbol from the plurality of data symbols in a data portion of the received packet, and repeating the updating upon receipt of at least one subsequent data symbol from the plurality of data symbols in the data portion of the received packet.
 51. The wireless communications device according to claim 50 further comprising storing the channel estimate reference in a channel estimate database at a receiver device.
 52. The wireless communications device according to claim 51 further comprising: for each data symbol, demodulating the data symbol based upon the channel estimate; decoding the data symbol; generating training data by remodulating the decoded data symbol; updating the channel estimate database based upon the training data; and generating the channel estimate for the data symbol based upon the updated channel estimate database.
 53. The wireless communications device according to claim 50 further comprising transforming the channel estimate to a frequency domain prior to updating the channel estimate reference to provide a time smoothed channel estimate reference.
 54. The wireless communications device according to claim 50 further comprising: for each subsequent data symbol during the updating, pipelining demodulating and modulating; and updating the channel estimate reference by performing a forward error correction (FEC) decoding.
 55. The wireless communications device according to claim 50 wherein the multiple access packet based communication network comprises an orthogonal frequency-division multiplexing (OFDM) wireless network.
 56. The wireless communications device according to claim 55 wherein the updating of the channel estimate reference comprises calculations based upon at least one of a frequency domain statistic of a radio channel at an OFDM symbol rate, a time domain statistic of the radio channel across OFDM symbols, and outcomes of each decoded OFDM symbol.
 57. The wireless communications device according to claim 50 wherein the updating is performed on a data symbol-by-data symbol basis.
 58. The wireless communications device according to claim 50 wherein each previously received data symbol is decoded iteratively with each subsequent data symbol.
 59. The wireless communications device according to claim 50 wherein the updating based upon the current and the at least one previously received data symbols is performed in parallel. 